10th International Semantic Web Conference
Semantic Web Challenge Chair at ISWC2011
Most of the functions of our society are based on networks designed during the late 1800s, and are modelled after centralized water systems. The rapid spread of ubiquitous networks, and connected sensors such as those contained in smartphones and cars, allow these networks to be reinvented as much more active and reactive control networks at the scale of the individual, the family, the enterprise, the city and the nation. This will fundamentally transform the economics of health, finance, logistics, and transportation. One key challenge is access to the personal data at scale to enable these systems to function more efficiently. In discussions with key CEOs, regulators, and NGOs at the World Economic Forum we have constructed a `new deal on data' that can allow personal data to emerge as accessible asset class that provides strong protection for individuals. The talk will also cover a range of prototype systems and experiments developed at MIT, outline some of the challenges and growth opportunities, focusing on how this new data ecosystem may end up strongly promoting but also shaping the semantic web.
Building A Nervous System for Society: The �New Deal on Data� and how to make Health, Financial, Logistics, and Transportation Systems Work
At 10 years of age, there is little doubt that the Semantic Web is an engineering success, with substantial (and growing) take-up in business, government and media. However, as a scientific field, have we discovered any general principles? Have we uncovered any universal patterns that give us insights into the structure of data, information and knowledge, patterns that are valid beyond the engineering of the Semantic Web in its current form? If we would build the Semantic Web again, surely some things would end up looking different, but are there things that would end up looking the same, simply because they have to be that way?
10 Years of Semantic Web research: Searching for universal patterns
The Web of Linked Data contains about 25 billion RDF triples and almost half a billion links across data sources; it is becoming a great asset for semantic applications. Linked Data comprises large general-purpose knowledge bases like DBpedia, Yago, and Freebase, as well as many reference collections in a wide variety of areas, spanning sciences, culture, entertainment, and more. Notwithstanding the great potential of Linked Data, this talk argues that there are significant limitations that need to be overcome for further progress. These limitations regard data scope and, especially, data quality. The talk discuss these issues and approaches to extending and enriching Linked Data, in order to improve its scope, quality, interpretability, cross-linking, and usefulness.
There are now more than half a dozen journals that publish semantic-web related research, and it is often difficult for authors to navigate this landscape. In this panel discussion, the editors from these journals will help the authors understand the different focus of each journal, the rewards and perils of journal publishing, and the steps in preparing their research for journal publications. The editors will then answer the authors' questions.
DC Proposal: Model for News Filtering with Named Entities
In this paper we introduce the project of our PhD thesis. The subject is a model for news articles ﬁltering. We propose a framework combining information about named entities extracted from news articles with article texts. Named entities are enriched with additional attributes crawled from semantic web resources. These properties are then used to enhance the ﬁltering results. We described various ways of a user proﬁle creation, using our model. This should enable news ﬁltering covering any speciﬁc user needs. We report on some preliminary experiments and propose a complex experimental environment and diﬀerent measures.
Semantic Web technologies are becoming more interleaved withgeospatial databases, which should lead to an easier integration and querying of spatial data. This is fostered by a growing amount of publicly available geospatial data like OpenStreetMap. However, the integration can lead to geographic inconsistencies and contradictions when combining multiple knowledge base. Having the integration in place, users might not issue standard queries and search points-of-interest, but rather interested in regions with specific attributes assigned to them. Though, having large amounts of spatial data available, standard databases and reasoners do not provide the means for (quantitative) spatial queries, or struggle to answer them efficiently. We seek to combine spatial reasoning, (nonmonotonic) logic programming, and ontologies for integrating geospatial databases and Semantic Web technologies. The focus of our investigation will be on a modular design, on efficiently process large amounts of spatial data, and on enabling default reasoning. We propose a two-tier design related to HEX-programs, which should lead to a plausible trade-off between modularity and efficiency. Furthermore, we consider suitable geo-ontologies to semantically annotate and link different geospatial sources. Finally, the findings should lead to a proof-of-concept implementation, which will be tested for efficiency and modularity in artificial and real-world use cases.
DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables
The explosion of digital content and the heterogeneity of enterprise content sources have resulted in a pressing need for advanced tools and technologies, to support enterprise content search and analysis. Semantic technology and linked data may be the long term solution to this growing problem. Our research explores the application of access control to a knowledge discovery platform. In order to ensure integrated information is only accessible to authorised individuals, existing access control policies need to be associated with the data. Through in-depth analysis we aim to propose an access control model and enforcement framework which can be used to represent and enforce various access models both inside and outside the enterprise. Furthermore, through experimentation we plan to develop a methodology which can be used as a guideline for the lifting of distributed access control policies from the individual data sources to a linked data network.
Vast amounts of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information beneﬁts from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-speciﬁc background knowledge. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. A table’s meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.
DC Proposal: PRISSMA, Towards Mobile Adaptive Presentation of the Web of Data
Ontology learning - loosely, the process of knowledge extrac-tion from diverse data sources - provides (semi-) automatic support forontology construction. As the `Web of Linked Data' vision of the Seman-tic Web is coming true, the `explosion' of Linked Data provides morethan sufficient data for ontology learning algorithms in terms of quan-tity. However, with respect to quality, notable issue of noises (e.g.,partialor erroneous data) arises from Linked Data construction. Our doctoralresearches will make theoretical and engineering contribution to ontologylearning approaches for noisy Linked data. More exactly, we will use theapproach of Statistical Relational Learning (SRL) to develop learningalgorithms for the underlying tasks. In particular, we will learn OWLaxioms inductively from Linked Data under probabilistic setting, andanalyze the noises in the Linked Data on the basis of the learned ax-ioms. Finally, we will make the evaluation on proposed approaches withvarious experiments.
The Mobile Web is evolving fast and mobile access to the Web of Data is gaining momentum. Interlinked RDF resources consumed from portable devices need proper adaptation to the context in which the action is performed. This paper introduces PRISSMA (Presentation of Resources for Interoperable Semantic and Shareable Mobile Adaptability), a domain-independent vocabulary for displaying Web of Data resources in mobile environments. The vocabulary is the ﬁrst step towards a declarative framework aimed at sharing and re-using presentation information for context-adaptable user interfaces over RDF data.
DC Proposal: Automatically transforming keyword queries to SPARQL on large-scale knowledge bases
Most Web of Data applications focus mainly on using SPARQL for issuing queries. This leads to the Web of Data being diﬃcult to access for non-experts. Another problem that will intensify this challenge is when applying the algorithms on large-scale and decentralized knowledge bases. In the current thesis, ﬁrstly we focus on the methods for transforming keyword-based queries into SPARQL automatically. Secondly, we will work on improving those methods in order to apply them on (a large subset of) the Linked Data Web. In an early phase, a heuristic method was proposed for generating SPARQL queries out of arbitrary number of keywords. Its preliminary evaluation showed promising results. So, we are working on the possible improvements for applying that on the large-scale knowledge bases.
The amount of Linked Data containing statistics is increasing; and so is the need for concepts of consuming it. Yet there are challenges, e.g., heterogeneous ways to describe quality information, mathematical functions, and categorisation hierarchies. In order to automatically, flexibly, and scalable integrate statistical Linked Data for expressive analysis we propose to use Semantic Web ontologies to build and evolve a well-interlinked conceptual model of statistical data for Online Analytical Processing.
DC Proposal: Automation of Service lifecycle on the Cloud by using Semantic technologies
Managing virtualized services efficiently over the cloud is an open challenge. We propose a semantically rich, policy-based framework to automate the lifecycle of cloud services. We have divided the IT service lifecycle into the five phases of requirements, discovery, negotiation, composition, and consumption. We detail each phase and describe the high level ontologies that we have developed to describe them. Our research complements previous work on ontologies for service descriptions in that it goes beyond simple matchmaking and is focused on supporting negotiation for the particulars of IT services.
Most Web of Data applications focus mainly on using SPARQLfor issuing queries. This leads to the Web of Data being difficult to accessfor non-experts. Another problem that will intensify this challenge iswhen applying the algorithms on large-scale and decentralized knowledgebases. In the current thesis, rstly we focus on the methods for transformingkeyword-based queries into SPARQL automatically. Secondly,we will work on improving those methods in order to apply them on(a large subset of) the Linked Data Web. In an early phase, a heuristicmethod was proposed for generating SPARQL queries out of arbitrarynumber of keywords. Its preliminary evaluation showed promising results.So, we are working on the possible improvements for applying thaton the large-scale knowledge bases.
DC Proposal: Towards a Framework for Efficient Query Answering and Integration of Geospatial Data
Semantic Web technologies are becoming more interleaved with geospatial databases, which should lead to an easier integration and querying of spatial data. This is fostered by a growing amount of publicly available geospatial data like OpenStreetMap. However, the integration can lead to geographic inconsistencies when combining multiple knowledge bases. Having the integration in place, users might not just issue a points-of-interest search, but rather might be interested in regions with speciﬁc attributes assigned to them. Though, having large amounts of spatial data available, standard databases and reasoners do not provide the means for (quantitative) spatial queries, or struggle to answer them efﬁciently. We seek to combine spatial reasoning, (nonmonotonic) logic programming, and ontologies for integrating geospatial databases with Semantic Web technologies. The focus of our investigation will be on a modular design, on efﬁcient processing of large amounts of spatial data, and on enabling default reasoning. We propose a two-tier design related to HEX-programs, which should lead to a plausible trade-off between modularity and efﬁciency. Furthermore, we consider suitable geo-ontologies to semantically annotate and link different sources. Finally, the ﬁndings should lead to a proof-of-concept implementation, which will be tested for efﬁciency and modularity in artiﬁcial and real-world use cases. Background and Problem Statement Fostered by a popular demand for location-aware search applications, linking and querying spatial data has become an active research ﬁeld. At the same time, governments open up their ofﬁcial datasets for public use, and collaborative projects like OpenStreetMap (OSM) are becoming large sources of spatial data (http://www.openstreetmap.org/). In this context, geospatial databases are the backbone for storing and querying these data. Hence they have been extensively studied by the Geographic Information Systems (GIS) community (cf. [9, 18]). Geospatial databases often have the drawback that querying them is complicated, inference mechanisms are virtually non-existent, and extending them is difﬁcult. In response, Semantic Web technologies are becoming more interleaved with geospatial databases [5, 24], which should lead to an easier integration and querying of spatial Supported by the Austrian Research Promotion Agency (FFG) project P828897, the Marie Curie action IRSES under Grant No. 24761 (Net2), and the EC project OntoRule (IST-2009-231875).
DC Proposal: Ontology Learning from Noisy Linked Data
Mobile devices like smartphones together with social networks enable people to generate, share, and consume enormous amounts of media content. Common search operations, for example searching for a music clip based on artist name and song title on video platforms such as YouTube, can be achieved both based on potentially shallow human-generated metadata, or based on more profound content analysis, driven by Optical Character Recognition (OCR) or Automatic Speech Recognition (ASR). However, more advanced use cases, such as summaries or compilations of several pieces of media content covering a certain event, are hard, if not impossible to fulfill at large scale. One example of such event can be a keynote speech held at a conference, where, given a stable network connection, media content is published on social networks while the event is still going on.In our thesis, we develop a framework for media content processing, leveraging social networks, utilizing the Web of Data and fine-grained media content addressing schemes like Media Fragments URIs to provide a scalable and sophisticated solution to realize the above use cases: media content summaries and compilations. We evaluate our approach on the entity level against social media platform APIs in conjunction with Linked (Open) Data sources, comparing the current manual approaches against our semi-automated approach. Our proposed framework can be used as an extension for existing video platforms.
Ontology learning loosely, the process of knowledge extraction from diverse data sources provides (semi-) automatic support for ontology construction. As the ‘Web of Linked Data’ vision of the Semantic Web is coming true, the ‘explosion’ of Linked Data provides more than suﬃcient data for ontology learning algorithms in terms of quantity. However, with respect to quality, notable issue of noises (e.g., partial or erroneous data) arises from Linked Data construction. Our doctoral researches will make theoretical and engineering contribution to ontology learning approaches for noisy Linked Data. More exactly, we will use the approach of Statistical Relational Learning (SRL) to develop learning algorithms for the underlying tasks. In particular, we will learn OWL axioms inductively from Linked Data under probabilistic setting, and analyze the noises in the Linked Data on the basis of the learned axioms. Finally, we will make the evaluation on proposed approaches with various experiments. Motivation Ontology learning refers to the task of providing (semi-) automatic support for ontology construction [3], and can overcome the knowledge acquisition bottleneck brought by the tedious and cumbersome task of manual ontology construction [17]. Recent ontology learning approaches have attempted to learn ontology from various types of data sets, such as text, xml, and database, but they seldom explore learning from Linked Data. Based on URIs, HTTP and RDF, the Linked Data project [2] aims to expose, share and connect related data from diverse sources on the Semantic Web. Linked Open Data (LOD) is a community eﬀort to apply the Linked Data principles to data published under open licenses. With this eﬀort, a large number of LOD data sets have been gathered in the LOD cloud, such as DBpedia, Freebase and FOAF proﬁles. LOD has gained rapid progress and is still growing constantly. Until May 2009, there are 4.7 billion RDF triples and around 142 million RDF links [2]. After that, the total has been increased to 16 billion triples in March 2010 and another 14 billion triples have been published by the AIFB according to [21].
DC Proposal: Towards an ODP Quality Model
Expertise modeling has been the subject of extensive research in two main disciplines - Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA techniques build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data, there has been a significant shift from static to evolving documents, characterized by micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide an all-encompassing, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) fine-grained provenance, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on micro-contributions, and (iii) collaboration networks - to create and enrich expertise profiles in community-centered environments.
The study of ontology design patterns (ODPs) is a fairly recent development. Such patterns simplify ontology development by codifying and reusing known best practices, thus lowering the barrier to entry of ontology engineering. However, while ODPs appear to be a promising addition to research and while such patterns are being presented and used, work on patterns as artifacts of their own, i.e. methods of developing, identifying and evaluating them, is still uncommon. Consequently, little is known about what ODP features or characteristics are beneﬁcial or harmful in diﬀerent ontology engineering situations. The presented PhD project aims to remedy this by studying ODP quality characteristics and what impact these characteristics have on the usability of ODPs themselves and on the suitability of the resulting ontologies. Research Problem In spite of the development of several well-deﬁned ontology construction methods, eﬃcient ontology development continues to be a challenge. One reason for this is that such work requires both extensive knowledge of the domain being modeled, and a suﬃcient understanding of ontology languages and logical theory. Ontology Design Patterns (ODPs) are considered a promising contribution to this challenge [2]. Such patterns encode best practices, which helps reduce the need for extensive experience when developing ontologies. Additionally, by promoting a limited set of best practice outlooks on common problems, patterns help harmonize how these problems are viewed, supporting interoperability of ontologies developed using them. Previous study into the state of ODP research indicates that while patterns are being used as tools in the ontology research community, and while there are patterns being developed and presented, patterns are rarely studied as artifacts of their own. The amount of work done on evaluation of patterns and pattern development and usage methods is limited [10]. There exists no established theory or model of ODP quality, and consequently there is no way of telling with certainty what is a good pattern for solving a particular type of problem based on pattern features. We will within the presented PhD project attempt to remedy this situation, by studying what quality characteristics or features of Ontology Design Patterns that are beneﬁcial in diﬀerent types of ontology engineering situations, and inversely, what such features or characteristics that could be considered harmful. To this end, three research questions have been established:
DC Proposal: Enriching Unstructured Media Content About Events to Enable Semi-Automated Summaries, Compilations, and Improved Search by Leveraging Social Networks
Skeletal dysplasias comprise a group of genetic diseasescharacterized by highly complex, heterogeneous and sparse data. Performingefficient and automated knowledge discovery in this domain poses seriouschallenges, one of the main issues being the lack of a proper formalization.Semantic Web technologies can, however, provide the appropriate means forencoding the knowledge and hence enabling complex forms of reasoning. Weaim to develop decision support methods in the skeletal dysplasia domain byapplying uncertainty reasoning over Semantic Web data. More specifically, wedevise techniques for semi-automated diagnosis and key disease featureinferencing from an existing pool of patient cases – that are shared anddiscussed in the SKELETOME community-driven knowledge curationplatform. The outcome of our research will enable clinicians and researchers toacquire a critical mass of structured knowledge that will sustain a betterunderstanding of these genetic diseases and foster advances in the field.
DC Proposal: Towards Linked Data Assessment and Linking Temporal Facts
Since the Linked Data is continuously growing on the Web, the quality of overall data can rapidly degrade over time. The research proposed here deals with the quality assessment in the Linked Data and the temporal linking techniques. First, we conduct an in-depth study of appropriate dimensions and their respectively metrics by deﬁning a data quality framework that evaluates, along these dimensions, linked published data on the Web. Second, since the assessment and improvement of the Linked Data quality such as accuracy or the resolution of heterogeneities is performed through record linkage techniques, we propose an extended technique that apply time in similarity computation which can improve over traditional linkage techniques. This paper describes the core problem, presents the proposed approach, reports on initial results, and lists planned future tasks.
Trust plays an important part in people's decision processes for using information. This is especially true on the Web, which has less quality control for publishing information. Untrustworthy data may lead users to make wrong decisions or result in misunderstanding concepts. Therefore, it is important for users to have a mechanism for assessing the trustworthiness of the information they consume. Prior research focuses on policy-based and reputation-based trust. It does not take the information itself into account. In this PhD research, we focus on evaluating the trustworthiness of Web content based on available metadata that can be obtained using Semantic Web technologies. This paper discusses the vision of our PhD work and presents an approach to solve that problem.
DC Proposal: Online Analytical Processing of Statistical Linked Data
In this paper we introduce the project of our PhD thesis. The subject is a model for news articles filtering. We propose a framework combining information about named entities extracted from news articles with article texts. Named entities are enriched with additional attributes crawled from semantic web resources. These properties are then used to enhance the filtering results. We described various ways of user profile creation, using our model. This should enable news filtering covering any specific user needs. We report on some preliminary experiments and propose a complex experimental environment and different measures.
The amount of Linked Data containing statistics is increasing; and so is the need for concepts of analysing these statistics. Yet, there are challenges, e.g., discovering datasets, integrating data of diﬀerent granularities, or selecting mathematical functions. To automatically, ﬂexibly, and scalable integrate statistical Linked Data for expressive and reliable analysis, we propose to use expressive Semantic Web ontologies to build and evolve a well-interlinked conceptual model of statistical data for Online Analytical Processing.
DC Proposal: Capturing Knowledge Evolution and Expertise in Community-driven Knowledge Curation Platforms
Expertise modeling has been the subject of extensive research in two main disciplines Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA techniques build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data, there has been a significant shift from static to evolving documents, characterized by micro-contributions. Thus, the existing macroperspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide an all-encompassing, domainagnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) finegrained provenance, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on microcontributions, and (iii) collaboration networks to create and enrich expertise profiles in community-centered environments.
Vast amounts of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table's meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. A table's meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.
DC Proposal: Knowledge Based Access Control Policy Specification and Enforcement
The Mobile Web is evolving fast and mobile access to the Web of Data is gaining momentum. Interlinked RDF resources consumed from portable devices need proper adaptation to the context in which the action is performed. This paper presents PRISSMA (Presentation of Resources for Interoperable Semantic and Shareable Mobile Adaptability), a domain-independent vocabulary for displaying Web of Data resources in mobile environments. The vocabulary is aimed at declaring, sharing and re-using presentation information for context-adaptable user interfaces over RDF data. The paper provides a discussion about presentation of Web of Data resources in mobile environments and describes PRISSMA core features, along with the most relevant future evolutions.
DC Proposal: Decision support methods in community-driven knowledge curation platforms
Skeletal dysplasias comprise a group of genetic diseases characterized by highly complex, heterogeneous and sparse data. Performing efficient and automated knowledge discovery in this domain poses serious challenges, one of the main issues being the lack of a proper formalization. Semantic Web technologies can, however, provide the appropriate means for encoding the knowledge and hence enabling complex forms of reasoning. We aim to develop decision support methods in the skeletal dysplasia domain by applying uncertainty reasoning over Semantic Web data. More specifically, we devise techniques for semi-automated diagnosis and key disease feature inferencing from an existing pool of patient cases – that are shared and discussed in the SKELETOME community-driven knowledge curation platform. The outcome of our research will enable clinicians and researchers to acquire a critical mass of structured knowledge that will sustain a better understanding of these genetic diseases and foster advances in the field.
The study of ontology design patterns (ODPs) is a fairly recent development. Such patterns simplify ontology development by codifying and reusing known best practices, thus lowering the barrier to entry of ontology engineering. However, while ODPs appear to be a promising addition to research and while such patterns are being presented and used, work on patterns as artifacts of their own, i.e. methods of developing, identifying and evaluating them, is still uncommon. Consequently, little is known about what ODP features or characteristics are beneficial or harmful in different ontology engineering situations. The presented PhD project aims to remedy this by studying ODP quality characteristics and what impact these characteristics have on the usability of ODPs themselves and on the suitability of the resulting ontologies.
DC Proposal: Evaluating Trustworthiness of Web Content using Semantic Web Technologies
Since the Linked Data is continuously growing on the Web, the quality of overall data can rapidly degrade over time. The research proposed here deals with the quality assessment in the Linked Data and the temporal linking techniques. First, we conduct an in-depth study of appropriate dimensions and their respectively metrics by defining a data quality methodology as a set of guidelines and techniques that defines a rational process to assess and improve the quality of data. Second, since the linking of entities is used to assess and improve the quality of data, we show that applying decay in similarity computation can already improve over traditional linkage techniques. This paper describes the core problem, presents the proposed approach, reports on initial results, and lists planned future tasks
Trust plays an important part in people’s decision processes for using information. This is especially true on the Web, which has less quality control for publishing information. Untrustworthy data may lead users to make wrong decisions or result in the misunderstanding of concepts. Therefore, it is important for users to have a mechanism for assessing the trustworthiness of the information they consume. Prior research focuses on policy-based and reputation-based trust. It does not take the information itself into account. In this PhD research, we focus on evaluating the trustworthiness of Web content based on available and inferred metadata that can be obtained using Semantic Web technologies. This paper discusses the vision of our PhD work and presents an approach to solve that problem.
Describing Services with the USDL Linked Data Vocabulary
Self-Service Linked Data Applications with the Information Workbench
Linked Data Based Enterprise Data Integration
Practical applications of Semantic Wikis in commercial environments
F# Type Providers – Unleashing the Semantic Web with Programming Languages
A common metadata approach to support eGovernment interoperability
Why Semantics Makes Sense for News Agencies
Multi-Tenant Content-Addressable RDF Storage with Native SPARQL Compilation
Automated Generation of Semantic Service Descriptions by Leveraging on Service Governance Models
Semantic Mediation Bus™: An Ontology-based Runtime Infrastructure for Service Interoperability
Refinder: A Semantic Social Productivity Tool
Interactive Data Integration with MappingAssistant
Due to the heterogeneity of distributed systems data integration is a main success factor in real-life business. Applying semantic web technologies for matching data is one successful approach for data integration. Ontoprise uses ontologies as target schema for integrating diﬀerent sources like databases, text-ﬁles and ontologies. However, the created target ontology and the corresponding mapping-rules might be error-prone. Hence, we developed the conﬂict resolution framework MappingAssistant which detects wrong rules or facts on the instance level in an interactive way. In this demo we present the MappingAssistant framework and an evaluation which emphasizes that users are used to investigate data on the instance level.
inContext Sensing: LOD augmented sensor data
In this demo paper, we present a system that shows how users with no expertise in sensor data can beneﬁt of Linked Data and semantic annotations to make sense of raw sensor data. Our motivations are that (1) these users are becoming the main consumers of sensor data, but sensors conceptualisation do not consider their point of view and (2) so far, no application dynamically creates Linked Data for sensors (as the linked datasets are usually predeﬁned).
Castor: Using Constraint Programming to Solve SPARQL Queries
Semantic Navigator: Use of Semantic Data in Web Navigation
Semantic web search engines can take advantage of machineunderstandable data published on the Web to provide more precise search results and advanced query capabilities. Semantic data embedded in web documents (serialized as RDFa or microformats, for example) can be used in conjunction with a semantic web search engine to provide a better web navigation experience. We present Semantic Navigator, 1 a Mozilla Firefox extension than brings advantages of semantic search to ordinary users. Semantic Navigator detects semantic data in web documents and with the aid of a semantic web search engine, it enables users to easily navigate to related documents containing information about a selected resource or about one of its properties.
A Demonstration of DNS^3: a Semantic-Aware DNS Service
Abstract The Domain Name System (DNS) is a hierarchical and distributed database used to resolve domain names into IP addresses. The current Web infrastructure heavily relies on the DNS service to allow endusers to access Web pages and Web data using meaningful names (like “www.verisign.com”) rather than cryptic sequences of numbers (e.g., “69.58.181.89”). The main functionalities of the DNS have been speciﬁed more than 25 years ago and have not fundamentally evolved since then. In this paper, we propose to demonstrate DNS 3 , an extension of the current DNS service based on security mechanisms and semantic metadata. Speciﬁcally, we show how one can embed authoritative RDF triples using the current DNS protocol, and how the naming service can take advantage of the embedded semantic metadata to publish authoritative information about the domains, to improve the performance of domain resolution through prefetching, and to alert end-users of probable threats when visiting potentially harmful domains.
Towards Policy-aware Queries over Linked Data
GATE Mímir: Answering Questions Google Can’t
Free text makes up a large proportion of the vast amounts of information generated by modern society, and search engines such as Google are exceptionally good at ﬁnding, indexing and searching this. However, the rise of the Semantic Web and the publishing of increasingly large amounts of structured and interlinked data now means that useful information is distributed across multiple sources and in a variety of formats, which cannot be easily reconciled by these search engines as it is not amenable to free text search. Hence, questions which we may wish to ask of society’s collective knowledge cannot be easily answered. For example, it is diﬃcult to see how traditional search engines could be used to locate documents in which a person born in Sheﬃeld is being quoted. In this paper, we describe GATE M´ımir which indexes not only free text, but also semantic annotations and knowledge base data. The resulting multi-paradigm index allows us to search across multiple information sources in order to answer questions which are either infeasible or impossible to answer using current web search engines. GATE M´ımir GATE M´ımir 1 is a multi-paradigm information management index and repository which can be used to index and search over text, annotations, semantic schemas (ontologies), and semantic metadata (instance data). It allows queries that arbitrarily mix full-text, structural, linguistic and semantic queries, and that can scale to gigabytes of text. We brieﬂy describe in this paper the underlying architecture; full details can be found in [1]. A GATE M´ımir index supports combinations of the following data types: Text: Support for full text search represents the most basic indexing functionality, and it is required in most (if not all) cases. In GATE M´ımir this is implemented as an MG4J 2 inverted index. Annotations: The ﬁrst step in abstracting away from plain text document content is the production of annotations. Annotations in this case are linguistic metadata associated with text snippets in the documents. For example, if the 1 Open source and available via http://gate.ac.uk/family/mimir.html 2 http://mg4j.dsi.unimi.it/
Sewelis: Exploring and Editing and RDF Base in an Expressive and Interactive Way
Query-based Faceted Search (QFS), introduced in a research paper at ISWC’11, reconciles the expressiveness of querying languages (e.g., SPARQL), and the beneﬁts of exploratory search found in faceted search. Because of the interactive nature of QFS, which is diﬃcult to fully render in a research paper, we feel it is important to complement it with a demonstration of our QFS prototype, Sewelis (aka. Camelis 2). An important addition to the research paper is the extension of QFS to the guided edition of RDF bases, where suggestions are based on existing data. This paper motivates our approach, shortly presents Sewelis, and announces the program of the demonstration. Screencasts of the demonstration, as well as material (program and data) to reproduce it, are available at http://www.irisa.fr/LIS/softwares/sewelis. Motivation A challenge of the Semantic Web is to enable the largest audience to explore and edit the knowledge expressed in the Semantic Web languages, in particular RDF(S) and OWL [6]. Because of the complexity of those languages, existing tools generally make a trade-oﬀ between expressiveness, i.e., the coverage of the chosen language, and usability. At one end of the spectrum, there are SPARQL endpoints and OWL editors (e.g., Prot´ eg´ e), which allow for full expressiveness but require advanced logical and technical knowledge. At the other end, there are Web applications that completely hide technical details but have limited functionalities (e.g., RSS). In between, there are graph visualization systems (e.g., Bramble [9]) and faceted search systems (e.g., SlashFacet [5], BrowseRDF [8], gFacet [3]) that oﬀer a ﬂexible and guided exploration of RDF graphs. However, those systems are far from covering the expressiveness of SPARQL (e.g., only paths of properties can be expressed). Concerning the edition of a RDF base, there exist tools that abstract away from the concrete syntax of formal languages, and guide users [7,2]. However, while their guiding generally takes into account language grammars and domain ontologies, it lacks of accuracy because it does not take into account the existing objects and their descriptions.
Representing Text Mining Results for Structured Pharmacological Queries
Several approaches integrating life science data using Semantic Web technologies have been described in the literature . However, these approaches have largely ignored the vast amount of content only available within the scientific literature. In this article, we present an RDF schema for text mining results that enables queries in SPARQL over textual and database data together. We show how real pharmacological queries can be answered over 4 billion text mined triples.
Combining N-gram Retrieval with Weights Propagation on Massive RDF Graphs
An Ontological Framework for Adaptive Feedback to Support Students while Programming
This paper presents a global framework based on ontologies to generate effective feedback for students getting error messages while learning to program. The proposed framework includes several ontologies: one of the domain, one of possible mistake types and one of the causes of these mistakes. They are connected by an intermediary ontology. The feedback is adaptive and depends on contents or students profiles (derived from interaction data) and is supported by a fifth ontology. Possible compilation errors in an introductory C programming course are presented as an example of this framework.
KC-Viz is a novel tool for visualizing and navigating ontologies, realised as a plug-in of the NeOn Toolkit. KC-Viz provides a comprehensive set of features to support ontology visualization and navigation, including an innovative mechanism for generating overviews of very large ontologies, which is based on an empirically validated ontology summarization algorithm. In this paper, we present an overview of the tool, in particular focusing on the features introduced in the latest version (v1.3.1).
Compared with the hypertext Web, Linked Data can satisfy more precise information needs, but presently still lacks tool support for citizen users. In this demonstration, we introduce a personalizable Linked Data browser called MyView, which enables users to query Linked Data by navigation (such as link traversal and ﬁltering) from one entity collection to another. With MyView, users can reuse their past queries in various ways, including categorizing favored links as diﬀerent views, assembling complex links with existing ones, and revisiting past queries via history and bookmark mechanisms. As an intelligent system, MyView evaluates queries in a logic programming fashion which supports reasoning, and its implementation features several strategies for dealing with the distributed, open, large-scale Web environment. It also generates explainable answers that carry provenance information, and supports source control. Finally, it interacts with users to resolve entity coreference for discovering more sources and reducing redundancy.
OntoFM: A Personal Ontology-based File Manager for the Desktop
Personal ontologies have been proposed as a means to support the semantic management of user information. Assuming that a personal ontology system is in use, new tools have to be developed at user interface level to exploit the enhanced capabilities offered by the system. In this work, we present an ontology-based file manager that allows semantic searching on the user’s personal information space. The file manager exploits the ontology relations to present files associated with specific concepts, proposes new related concepts to users, and helps them explore the information space and locate the required file.
In this paper, three alternative implementations of the POWDER W3C Recommendation are presented, compared, and discussed. The main issue with implementing POWDER is that POWDER inference accesses resources’ URIs in order to mass-annotate regular expressiondelineated groupings of URIs.
Identifying User Interests in Folksonomies
This paper proposes a probabilistic method for classifying folksonomy users to speciﬁc domains and for identifying their speciﬁc interests to these domains. The proposed method uses a hierarchical probabilistic topic modeling approach that exploits tags to induce hierarchies of latent topics. These hierarchies represent domain conceptualizations of speciﬁc domains that are either collective or user-speciﬁc. We propose two alternative methods that exploit the induced hierarchies for classifying and identifying users’ interests to speciﬁc domains and provide preliminary evaluation results.
RMonto: ontological extension to RapidMiner
We present RMonto, an ontological extension to RapidMiner, that provides possibility of machine learning with formal ontologies. RMonto is an easily extendable framework, currently providing support for unsupervised clustering with kernel methods and (frequent) pattern mining in knowledge bases. One important feature of RMonto is that it enables working directly on structured, relational data. Additionally, its custom algorithm implementations may be combined with the power of RapidMiner through transformation/extraction from the ontological data to attribute-value data.
Synth – Linked Data Application Implementation Environment
In this demo, we present Synth, a new development environment to implement Linked Data applications. It is best used in conjunction with the SHDM method, making it possible to take any RDF data available on the Linked Data cloud, extend it with one’s own data, and provide a Web application that exposes and manipulates this data to perform a given set of tasks, including navigation, as well as general business logic.
One Simple Ontology for Linked Data Sets
The Linking Open Data (LOD) cloud includes over 26 billion RDF triples from various domains. In order to access linked data sets, Semantic Web users have to understand the ontology schema of the data sets. However, understanding all the ontologies used in the LOD cloud is not feasible and is time-consuming. A simple and easily understandable ontology that integrates ontology schema from diﬀerent data sets is a solution to this problem. This paper proposes an automatic ontology learning method that integrates ontologies from diﬀerent linked data sets, and presents case studies to show the advantages of our approach.
Evaluation of Linked Data entity extraction tools
NERD: A Framework for Evaluating Named Entity Recognition Tools in the Web of Data
In this paper, we present NERD, an evaluation framework we have developed that records and analyzes ratings of Named Entity (NE) extraction and disambiguation tools working on English plain text articles performed by human beings. NERD enables the comparison of different popular Linked Data entity extractors which expose APIs such as AlchemyAPI, DBPedia Spotlight, Extractiv, OpenCalais and Zemanta. Given an article and a particular tool, a user can assess the precision of the named entities extracted, their typing and linked data URI provided for disambiguation and their subjective relevance for the text. All user interactions are stored in a database. We propose the NERD ontology that deﬁnes mappings between the types detected by the diﬀerent NE extractors. The NERD framework enables then to visualize the comparative performance of these tools with respect to human assessment. Key words: Entity extraction, Linked Data, Natural Language Processing, Evaluation of Linked Data entity extraction tools 1
DBpedia internationalization - a graphical tool for I18n infobox-to-ontology mappings
During the past two decades, the use of the Web has spread across multiple countries and cultures. While the Semantic Web is already served in many languages, we are still facing challenges concerning its internationalization. The DBpedia project, a community eﬀort to extract structured information from Wikipedia, is already supporting multiple languages. This paper presents a graphical tool for creating internationalized mappings for DBpedia.
Applying Linked Data to Media Fragments and Annotations
The Web applications today have been enriched with various multimedia resources and annotations. However, there is still a lack of semantic interlinking between media fragments and annotations, which leads to the insuﬃcient index of inside content of multimedia resources. This paper shows a demo of applying linked data principles in media fragments and annotations to improve the index of multimedia resources. Using linked data a media fragment can be universally identiﬁed by a URI and linked to annotations or other media fragments in the linked data cloud. The demo is based on the UK Parliament Debate scenario. The RDF ﬁle containing media fragments and annotations of the debate video has been published in Sindice semantic web index and linked to other resources in the linked data cloud.
A Personalized Mashup Using Rule-based Reasoning and Linked Data
In this paper we propose an intelligent personalization service, built upon the idea of combining Linked Data with Semantic Web rules. This service is mashing up information from different bookstores, and suggests users with personalized data according to their preferences. This information as well as the personalization rules are then processed and managed by a scalable knowledge repository. Finally, they are made available as Linked Data, thus enabling thirdparty recipients to consume knowledge-enhanced information.
RepOSE : A System for Debugging is-a Structure in Networked Taxonomies
Computing Fine-grained Semantic Annotations of Texts
(Extended Abstract) Yue Ma and Franc¸ois L´evy LIPN CNRS, UMR7030, Paris 13 University {firstname.name}@lipn.univ-paris13.fr 1
MetaMorphosis+ - A social network of educational Web resources based on semantic integration of services and data
Past research aiming at interoperability within the TechnologyEnhanced Learning (TEL) field has led to a fragmented landscape of competing metadata schemas and interface mechanisms. So far, Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web. We propose MetaMorphosis+, a social educational application which adopts a general approach to exploit existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain.
Tools for Pattern-Based Transformation of OWL Ontologies
OWL to English: a tool for generating organised easily-navigated hypertexts from ontologies
It has frequently been observed that domain experts are not necessarily ontology experts, and that the production of ontologies would be aided if they could read and edit axioms in natural language. The SWAT Tools Verbaliser is available, via a web interface, for verbalising OWL ontologies as texts in a controlled fragment of English. Taking as input any OWL ontology, the verbaliser creates a lexicon containing entries for all the entities in the input, and uses it to generate an English sentence corresponding to each logical axiom. These sentences are then organised into a document structure similar to that of an encyclopaedia, with an entry providing a deﬁnition, typology and examples for each entity. The output is either organised, easily-navigable English text encoded in XML, or a copy of the input OWL in which each entity is annotated with its description entry. The generated texts have been evaluated in a number of ways which are brieﬂy presented here.
LDIF - Linked Data Integration Framework
The Linked Data Integration Framework can be used within Linked Data applications to translate heterogeneous data from the Web of Linked Data into a clean local target representation while keeping track of data provenance. LDIF provides an expressive mapping language for translating data from the various vocabularies that are used on the Web into a consistent, local target vocabulary. LDIF includes an identity resolution component which discovers URI aliases in the input data and replaces them with a single target URI based on user-provided matching heuristics. For provenance tracking, the LDIF framework employs the Named Graphs data model. This paper describes the architecture of the LDIF framework and presents a performance evaluation of a life science use case.
Noisy Semantic Data Processing in Seoul Road Sign Management System
The Seoul Road Sign Management (RSM) is a system which provides the semantic integration of LOD’s Linked Geo Data and Open Street Map with Korean POI data set. That is an attempt to develop intelligent road sign management system based on the LarKC platform. The RSM data set contains over 1.1 billion triples of semantic data. However, signiﬁcant amount of the RSM data are noisy (e.g., inconsistent, partial, or erroneous). We have facilitated the RSM system with the capability of processing and reasoning with noisy semantic data, so that the RSM system is robust enough to return intended answers in spite of the poor quality of the semantic data.
DEW BEADS: A Framework for Distributional and Emergent Web Semantics
This is an extension of an accepted ISWC’11 research track contribution [1], which introduces an alternative, bottom-up and emergent conception of the Web semantics based on the distributional hypothesis (an approach that is rather complementary to the top-down Semantic Web standards based on logics and model theory). The promising potential of our proposal has been demonstrated in [1] by a thoroughly evaluated experiment in knowledge consolidation. In this more technically oriented demo paper, we augment [1] by an overview of DEW BEADS – an open source framework we implemented to test our research ideas 1 . We describe the framework’s architecture and features in Section 2. Section 3 then shows an example of DEW BEADS deployment to exploration of knowledge in life science publications. We also outline the general framework’s usage in custom applications there.
Semantic Smoothing for Twitter Sentiment Analysis
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classiﬁers from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character limit. In this work we propose using semantic smoothing to alleviate the data sparseness problem. Our approach extracts semantically hidden concepts from the training documents and then incorporates these concepts as additional features for classiﬁer training. We tested our approach using two diﬀerent methods. One is shallow semantic smoothing where words are replaced with their corresponding semantic concepts; another is to interpolate the original unigram language model in the Naive Bayes (NB) classiﬁer with the generative model of words given semantic concepts. Preliminary results show that with shallow semantic smoothing the vocabulary size has been reduced by 20%. Moreover, the interpolation method improves upon shallow semantic smoothing by over 5% in sentiment classiﬁcation and slightly outperforms NB trained on unigrams only without semantic smoothing.
Entendre: Interactive Semantic Feedback for Ontology Authoring
This demonstration presents Entendre, a framework to analyse ontology authors’ inputs and provide meaningful feedback at a semantic level. The feedback aims to make ontology authors aware of potential issues such as inconsistency, class unsatisﬁability, unexpected logical implications, redundancy and isolated entities. The implementation of Entendre that will be demonstrated, extends a CNL-based ontology authoring environment, allowing users without prior knowledge engineering experience to build ontologies while becoming aware of the implications of the formal semantics of OWL. An initial evaluation shows that the feedback is helpful to both novice and experienced ontology authors.
Semantically-driven recursive navigation and retrieval of data sources in the Web of Data
This paper introduces a semantically-driven recursive RDF link-based navigation system for the Web of Data. It combines the power of regular expressions with triggers based on SPARQL queries, to perform controlled exploration and retrieval of semantic data sources.
HDT-it: Storing, Sharing and Visualizing Huge RDF Datasets
Huge RDF Datasets are currently being published in the Linked-Open-Data cloud. Appropriate data structures are required to address scalability and performance issues when storing, sharing and querying these datasets. HDT (Header, Dictionary, Triples) is a binary format that represents RDF data in a compressed manner, therefore saving space whilst providing fast search operations directly on the compressed representation. These facts make it an excellent format when storing or sharing millions of triples. HDT-it is a tool that can generate and consume RDF ﬁles in HDT format. It demonstrates the capabilities of HDT by allowing to search basic triple patterns against HDT ﬁles, and also visualizes the 3D adjacency matrix of the underlying RDF graph to provide an overview of the dataset distribution.
We present a novel approach to the resource identity problem in RDF documents based on the notion of context-dependant facets.
TuneSensor: A Semantic-Driven Music Recommendation Service For Digital Photo Albums
Digital photo album softwares like iPhoto 1 have enjoyed great popularity for years. These years, online photo album services (e.g., Flickr 2 and Picasa 3 ) have been becoming more and more popular with the development of social Web. In this paper, we demonstrate our eﬀort called TuneSensor to recommend music for photo albums automatically. In particular, we exploit semantic data to represent both images and music. Furthermore, we leverage mining techniques to capture semantic relatedness between these diﬀerent types of multimedia data, which is the essential step for recommendation.
MovieGoer - Semantic Social Recommendations and Personalised Location-Based Offers
MovieGoer is an application designed for portable devices that provides its users with context-based services on movie schedule information. It uses social networks, such as Facebook, to replace a user’s eﬀort to enter preferences and personal data explicitly. It gives personalised recommendations on new movies and allows the users to interact with friends sharing the same movie taste. In addition, it enables new business models by connecting cinemas and other service providers with customers. The data integration for all the above is done using speciﬁc data sets and schemas from the Linked Open Data cloud.
TOPICA: A Tool for Visualising Emerging Semantics of POIs based on Social Awareness Streams
Topica is an application that enriches the Social Web with semantic data, to enable collective perception of Points of Interest (POIs), which are human constructs that describe information about locations (e.g., restaurants, attractions, cities). Topica provides an extra layer of information, compared to existing applications for browsing POIs, by modelling hidden characteristics of POIs, by: (1) generating a Linked Data representation of the collective perception of a POI; (2) enhancing the POI representation by mashing up services that enrich the POI’s related entities; and (3) providing a visual representation of the POIs adapted to suit userand context-sensitive ﬁlters. Topica identiﬁes topics relevant to a POI by extracting DBpedia categories from entities (e.g., People, Places) and keywords (e.g., Crete, Bonn) obtained from social awareness streams related to the POIs. Key words: Linked Data, Semantic Web, Point of Interest, Social Awareness Streams, citizen-sensing, social data mining, emerging semantics 1
ScienceWISE: A Web-based Interactive Semantic Platform for Scientific Collaboration
Information on the Web includes a huge diversity of opinions, viewpoints, sentiments, emotions, and biases. Accordingly, more and more methods, techniques and tools are available to extract these semantics from text. Representation and exchange of diversity-related information can be easily supported by the use of semantic technologies. For this, we introduce the Knowledge Diversity Ontology (KDO).
DeMoSt: a Tool for Exploring the Decomposition and the Modular Structure of OWL Ontologies
We intend to complete our paper "Decomposition and Modular Structure of BioPortal Ontologies", accepted for ISWC2011, with the presentation of DeMoSt, a tool to explore the decomposition and the modular structure of an ontology.
EventMedia: Visualizing Events and Associated Media
A wide variety of past and upcoming events are announced and described by several social online services. These web sites range from general event directories to local city guides that may have illustrating media. They often overlap in terms of coverage, and provide each their own social networks features to support users in sharing and deciding upon attending events. The information about the events, the social connections and the representative media are therefore all spread and locked in amongst these services providing overall limited event coverage and no interoperability of the description. In this paper, we present a web-based environment producing and consuming linked data to provide an explicit interlinking of event-related and up-to-date information. We propose interactive and user-friendly interfaces to visualize events with the aim to meet the user needs: relive experiences based on media, and support decision making for attending upcoming events.
A Semantic Knowledge Management Framework for Informal Communication Exchanges
Whilst formal organizational knowledge is often stored in archives and accessed through a range of IT tools, informal knowledge from face to face meetings, email exchanges, team meetings is usually not stored and remains implicit. This work aims at ﬁnding eﬀective ways of capturing and making use of informal communications, making it available through a knowledge management system to support tasks such as expertise ﬁnding and topic trends spotting. This paper presents an overview of the knowledge management system for informal communication exchanges, with details of how the knowledge is captured, searched and visualised.
Linked Data Quality Assessment through Network Analysis
Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is diﬃcult to obtain an overall picture as to whether the links introduced improve or diminish the quality of Linked Data. In this work, we present an extensible framework that allows for the assessment of Linked Data quality from a global perspective. We test the framework on a set of known quality links and show that it eﬀectively detects quality changes.
Semantator: A Semi-automatic Semantic Annotation Tool for Clinical Narratives
In this paper, we introduce Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, users can annotate document fragments with classes in the ontology to create instances and relate created instances with ontology properties. Also, Semantator enables automatic annotation by connecting to the NCBO annotator and the clinical Text Analysis and Knowledge Extraction Systems (cTAKES). By representing annotations in OWL, Semantator has basic reasoning capability based upon the underlying semantics of owl:disjointWith and owl:equivalentClass.
A prototypical OWL Full Reasoner based on First-Order Reasoning
We report on our ongoing endeavour to create a prototypical reasoning system for the OWL 2 Full ontology language and several of its sublanguages, including RDFS and the OWL 2 RL/RDF Rules. Among the languages speciﬁed by the W3C OWL 2 standard, OWL 2 Full is the most expressive one, and several other languages, such as RIF and SPARQL 1.1, have dependencies on OWL 2 Full, but to date no reasoner has been implemented for this language. The basic idea underlying our system is to translate the semantics speciﬁcation of OWL 2 Full and the RDF graphs representing the input ontologies into ﬁrst-order logic formulae, and to apply ﬁrst-order reasoners to this axiomatisation to perform one of the supported reasoning tasks: ontology consistency checking, entailment checking, or query answering. The paper explains the taken approach, summarizes the results of a recent evaluation of this approach, and gives an overview of the functionality, architecture and implementation of the reasoner. of OWL 2 Full is the RDF Abstract Syntax [5], i.e., every RDF graph is a valid OWL 2 Full ontology. The semantics of OWL 2 Full is the OWL 2 RDF-Based Semantics [6], which incorporates the semantics of RDFS and is speciﬁed like RDFS as a set of model-theoretic “semantic conditions”. The semantic conditions are given in the style of ﬁrst-order logic (FOL) formulae and provide formal meaning for certain combinations of RDF triples that represent OWL 2 language constructs. The expressivity of the semantics of OWL 2 Full is roughly comparable with that of OWL 2 DL [10], but it also applies to ontologies beyond the syntactic restrictions that OWL 2 DL deﬁnes to retain computational decidability; in fact, this extended ﬂexibility renders OWL 2 Full undecidable. However, due to design decisions originally made for RDF, certain kinds of OWL 2 DL
Scaling Data Linkage Generation with Domain-Independent Candidate Selection
We propose a candidate selection algorithm for scalably detecting coreferent instance pairs from heterogeneous Semantic Web data sources. Our algorithm selects candidate pairs by computing a characterlevel similarity on disambiguating literal values that are chosen using domain-independent unsupervised learning. We index the instances on such values to eﬃciently look up similar instances. Our algorithm is evaluated on three instance categories in two RDF datasets.
Assessing Health Effects of Water Pollution Using a Semantic Water Quality Portal
We demonstrate a semantically enabled approach for environmental monitoring as embodied in our semantic water quality portal. The portal assesses water quality utilizing two data sources, the United States Environmental Protection Agency (EPA) and the United States Geological Survey (USGS), by the user’s choice from a number of regulations, e.g. federal level regulations established by the EPA as well as state departments of environmental protection. The portal identifies pollution events using an OWLbased reasoning system and provides browsing facets generated from provenance data encoded using the Proof Markup Language (PML). We show how exposing these measurements and their provenance as semantic data enables them to be combined with additional external data sources to look for correlations between pollution levels and health effects seen in nearby populations. This submission highlights the interactive demonstration aspects of the portal and augments the more detailed technical description of the semantic infrastructure, reasoning, and benefits of the approach that has been accepted for presentation in the Semantic Web In Use track [1].
SEMLEX - A Framework for Visually Exploring Semantic Query Log Analysis
With organisations, local bodies and governments now releasing large amounts of linked data, there is a great opportunity for users and software agents to look for structured information, serving their information needs. Query logs, preserving such information needs can be harvested to understand what linked data consumers are looking for. Though statistical analysis of such query logs have been employed over the years to improve performance, visualising such analyses can provide a different way of exploration that could be invaluable to researchers, developers and linked data providers for discovering hidden trends and patterns. This paper presents our approach to analyse query logs and introduces SEMLEX, a tool that facilitates visual exploration of semantic query log analysis.
Semantic Index: Scalable Query Answering without Forward Chaining or Exponential Rewritings
Building SPARQL-Enabled Applications with Android Devices
In this paper, we show how features can be added to an Android device (a smartphone) to enable mobile applications to expose data through a SPARQL endpoint. Using simple query federation mechanisms, we describe a demonstrator illustrating how SPARQL-Enabled Android devices can allow us to rapidly develop applications mashing-up data from a collaborative network of sensor-based data sources.
SemPuSH: Privacy-Aware and Scalable Broadcasting for Semantic Microblogging
Users of traditional microblogging platforms such as Twitter face drawbacks in terms of (1) Privacy of status updates as a followee – reaching undesired people (2) Information overload as a follower – receiving uninteresting microposts from followees. In this paper we demonstrate distributed and user-controlled dissemination of microposts using SMOB (semantic microblogging framework) and Semantic Hub (privacy-aware implementation of PuSH 3 protocol) . The approach leverages users’ Social Graph to dynamically create group of followers who are eligible to receive micropost. The restrictions to create the groups are provided by the followee based on the hastags in the micropost. Both SMOB and Semantic Hub are available as open source.
Managing Linguistic Resources by Enriching Their Metadata with Linked Data
The NaLiDa project aims at contributing to an infrastructure for the metadata-based description and access to linguistic resources and tools. When aggregating heterogenous metadata sets from various providers to provide a single and uniform point of access to the aggregation, data curation becomes a central issue. In this paper, we describe how we use authority ﬁles from the German National Library, available as Linked Data, to tackle this issue for metadata ﬁelds about persons, organisations, and subject classiﬁcations.
A Completely Automatic Direct Mapping of Relational Databases to RDF and OWL
Integrating relational databases with the Semantic Web can be accomplish by means of two primary approaches: automatic direct mapping or developers detailing application speciﬁc mappings. Both approaches are the subject of the W3C Relational Database to RDF (RDB2RDF) Working Group. Intuitively, a direct mapping is a default and automatic way to translate a relational database schema and its content to OWL and RDF. In this poster, we present a speciﬁcation, expressed in Datalog, of a direct mapping inspired by the current Direct Mapping draft of the W3C RDB2RDF Working Group. We are currently studying four fundamental properties: monotonicity, information preservation, query preservation and semantics preservation. In particular, we observe that the combination of these properties needs to be addressed very carefully.
SPARQL Execution as Fast as SQL Execution on Relational Data
Relational Database to RDF (RDB2RDF) systems executes SPARQL queries on the relational data. Past studies have shown that RDB2RDF systems do not perform well, in other words, the execution time of a SPARQL query on a RDB2RDF system compared to its semantically equivalent SQL query is much slower. Therefore, we ask ourselves, what optimizations are needed in order to support effective SPARQL execution on relationally stored data? We experimented on Microsoft SQL Server, using the Barton and Berlin SPARQL Benchmark, and Ultrawrap, an automatic RDB2RDF wrapping system that has been architectured to leverage the SQL optimizer. Our initial results identify two important optimizations for effective SPARQL execution using Ultrawrap: detection of unsatisfiable conditions and self-join elimination.
Evaluating Adaptive Query Processing Techniques for Federations of SPARQL Endpoints
We present ANAPSID and illustrate the beneﬁts of adaptive semantic data management techniques for accessing a federation of SPARQL endpoints. ANAPSID adapts query execution schedulers to data availability and runtime conditions, implements physical SPARQL operators that detect when an endpoint becomes blocked or data trafﬁc is bursty, and opportunistically produces results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previous computed matches to secondary memory avoiding duplicates. We show ANAPSID performance with respect to a variety of RDF engines for queries of diverse complexity, and show that ANAPSID may outperform existing engines.
The Observation and Measurements ontology
Looking at CAREY: Controlling Climatological Regions in State of Emergency
We present CAREY, an alert system notiﬁcation for regions in state of emergency. CAREY implements the mediator-wrapper architecture on top of the Geospatial Web to visualize risky regions in terms of their weather conditions. We illustrate the formalization of the problem of detecting risky regions as a two-fold problem that relies on annotations of sensor data with the ontology of Observations and Measurements (O&M) to enhance state-of-the-art data mining and ranking techniques. First, sensor observations are clustered according to their Geospatial information then, proximate regions are clustered into micro-climate areas in terms of the similarity of their weather conditions. Finally, Top-k Skyline techniques are used to identify the top-k areas that best meet criteria of risk among the areas that are incomparable with respect to this condition. We demonstrate the capabilities of CAREY.
dipLODocus[RDF]--Short and Long-Tail RDF Analytics for Massive Webs of Data
The proliferation of semantic data on the Web requires RDF database systems to constantly improve their scalability and transactional eﬃciency. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. This paper introduces a novel database system for RDF data management called dipLODocus [RDF] , which supports both transactional and analytical queries eﬃciently. dipLODocus [RDF] takes advantage of a new hybrid storage model for RDF data based on recurring graph patterns. In this paper, we describe the general architecture of our system and compare its performance to state-of-the-art solutions for both transactional and analytic workloads.
Interoperability of financial information
Multilingual and cross-lingual ontology matching
A Machine Learning Approach to Multilingual and Cross-lingual Ontology Matching
Ontology matching is a task that has attracted considerable attention in recent years. With very few exceptions, however, research in ontology matching has focused primarily on the development of monolingual matching algorithms. As more and more resources become available in more than one language, novel algorithms are required which are capable of matching ontologies which share more than one language, or ontologies which are multilingual but do not share any languages. In this paper, we discuss several approaches to learning a matching function between two ontologies using a small set of manually aligned concepts, and evaluate them on diﬀerent pairs of ﬁnancial accounting standards, showing that multilingual information can indeed improve the matching quality, even in cross-lingual scenarios. In addition to this, as current research on ontology matching does not make a satisfactory distinction between multilingual and cross-lingual ontology matching, we provide precise deﬁnitions of these terms in relation to monolingual ontology matching, and quantify their eﬀects on diﬀerent matching algorithms.
Repairing Ontologies for Incomplete Reasoners
The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of ‘repairing’ an ontology T —that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪ R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not signiﬁcantly aﬀect reasoner performance.
Local Closed World Semantics: Grounded Circumscription for OWL
We present a new approach to adding closed world reasoning to the Web Ontology Language OWL. It transcends previous work on circumscriptive description logics which had the drawback of yielding an undecidable logic unless severe restrictions were imposed. In particular, it was not possible, in general, to apply local closure to roles. In this paper, we provide a new approach, called grounded circumscription, which is applicable to SROIQ and other description logics around OWL without these restrictions. We show that the resulting language is decidable, and we derive an upper complexity bound. We also provide a decision procedure in the form of a tableaux algorithm.
Inspecting regularities in ontology design using clustering
We propose a novel application of clustering analysis to identify regularities in the usage of entities in axioms within an ontology. We argue that such regularities will be able to help to identify parts of the schemas and guidelines upon which ontologies are often built, especially in the absence of explicit documentation. Such analysis can also isolate irregular entities, thus highlighting possible deviations from the initial design. The clusters we obtain can be fully described in terms of generalised axioms that oﬀer a synthetic representation of the detected regularity. In this paper we discuss the results of the application of our analysis to diﬀerent ontologies and we discuss the potential advantages of incorporating it into future authoring tools.
A Clustering-based Approach to Ontology Alignment
Ontology alignment is an important problem for the linked data web, as more and more ontologies and ontology instances get published for speciﬁc domains such as government and healthcare. A number of (semi-)automated alignment systems have been proposed in recent years. Most combine a set of similarity functions on lexical, semantic and structural features to align ontologies. Although these functions work well in many cases of ontology alignments, they fail to capture alignments when terms or structure varies vastly across ontologies. In this case, one is forced to rely on manual alignment. In this paper, we study whether it is feasible to re-use such expert provided ontology alignments for new alignment tasks. We focus in particular on many-to-one alignments, where the opportunity for re-use is feasible if alignments are stable. Speciﬁcally, we deﬁne the notion of a cluster as being made of multiple entities in the source ontology S that are mapped to the same entity in the target ontology T . We test the stability hypothesis that the formed clusters of source ontology are stable across alignments to diﬀerent target ontologies. If this hypothesis is valid, the clusters of an ontology S, built from an existing alignment with an ontology T , can be eﬀectively exploited to align S with a new ontology T ′ . Evaluation on both manual and automated high-quality alignments show remarkable stability of clusters across ontology alignments in the ﬁnancial domain and the healthcare and life sciences domain. Experimental evaluation also demonstrates the eﬀectiveness of utilizing the stability of clusters in improving the alignment process in terms of precision and recall.
Decomposition and Modular Structure of BioPortal Ontologies
Abstract We present the ﬁrst large scale investigation into the modular structure of a substantial collection of state-of-the-art biomedical ontologies, namely those maintained in the NCBO BioPortal repository. 5 Using the notion of Atomic Decomposition, we partition BioPortal ontologies into logically coherent subsets (atoms), which are related to each other by a notion of dependency. We analyze various aspects of the resulting structures, and discuss their implications on applications of ontologies. In particular, we describe and investigate the usage of these ontology decompositions to extract modules, for instance, to facilitate matchmaking of semantic Web services in SSWAP (Simple Semantic Web Architecture and Protocol). Descriptions of those services use terms from BioPortal so service discovery requires reasoning with respect to relevant fragments of ontologies (i.e., modules). We present a novel algorithm for extracting modules from decomposed BioPortal ontologies which is able to quickly identify atoms that need to be included in a module to ensure logically complete reasoning. Compared to existing module extraction algorithms, it has a number of beneﬁts, including improved performance and the possibility to avoid loading the entire ontology into memory. The algorithm is also evaluated on BioPortal ontologies and the results are presented and discussed.
Connecting the Dots: A Multi-pivot Approach to Data Exploration
The purpose of data browsers is to help users identify and query data eﬀectively without being overwhelmed by large complex graphs of data. A proposed solution to identify and query data in graph-based datasets is Pivoting (or set-oriented browsing ), a many-to-many graph browsing technique that allows users to navigate the graph by starting from a set of instances followed by navigation through common links. Relying solely on navigation, however, makes it diﬃcult for users to ﬁnd paths or even see if the element of interest is in the graph when the points of interest may be many vertices apart. Further challenges include ﬁnding paths which require combinations of forward and backward links in order to make the necessary connections which further adds to the complexity of pivoting. In order to mitigate the eﬀects of these problems and enhance the strengths of pivoting we present a multi-pivot approach which we embodied in tool called Visor. Visor allows users to explore from multiple points in the graph, helping users connect key points of interest in the graph on the conceptual level, visually occluding the remainder parts of the graph, thus helping create a road-map for navigation. We carried out an user study to demonstrate the viability of our approach.
FedX: Optimization Techniques for Federated Query Processing on Linked Data
Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on the Web, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for eﬃcient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an eﬀective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables eﬃcient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and eﬃciency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a signiﬁcant improvement in query performance over state-of-the-art federated query engines.
strukt---A Core Ontology for the Integration of Individual and Organizational Knowledge Work
Expert-driven business process management is an established means for improving eﬃciency of organizational knowledge work. Implicit procedural knowledge in the organization is made explicit by deﬁning processes. This approach is not applicable to individual knowledge work due to its high complexity and variability. However, without explicitly described processes there is no analysis and eﬃcient communication of best practices of individual knowledge work within the organization. In addition, the activities of the individual knowledge work cannot be synchronized with the activities in the organizational knowledge work. Solution to this problem is the semantic integration of individual knowledge work and organizational knowledge work by means of the patternbased core ontology strukt. The ontology allows for deﬁning and managing the dynamic tasks of individual knowledge work in a formal way and to synchronize them with organizational business processes. Using the strukt ontology, we have implemented a prototype application for knowledge workers and have evaluated it at the use case of an architectural oﬃce conducting construction projects.
Capturing Instance Level Ontology Evolution for DL-Lite
Blank nodes are deﬁned in RDF as ‘existential variables’ in the same way that has been used before in mathematical logic. However, evidence suggests that actual usage of RDF does not follow this deﬁnition. In this paper we thoroughly cover the issue of blank nodes, from incomplete information in database theory, over different treatments of blank nodes across the W3C stack of RDFrelated standards, to empirical analysis of RDF data publicly available on the Web. We then summarize alternative approaches to the problem, weighing up advantages and disadvantages, also discussing proposals for Skolemization.
Modelling and Analysis of User Behaviour in Online Communities
Understanding and forecasting the health of an online community is of great value to its owners and managers who have vested interests in its longevity and success. Nevertheless, the association between community evolution and the behavioural patterns and trends of its members is not clearly understood, which hinders our ability of making accurate predictions of whether a community is ﬂourishing or diminishing. In this paper we use statistical analysis, combined with a semantic model and rules for representing and computing behaviour in online communities. We apply this model on a number of forum communities from Boards.ie to categorise behaviour of community members over time, and report on how diﬀerent behaviour compositions correlate with positive and negative community growth in these forums.
Encyclopedic Knowledge Patterns from Wikipedia Links
What is the most intuitive way of organizing concepts for describing things? What are the most relevant types of things that people use for describing other things? Wikipedia and Linked Data oﬀer knowledge engineering researchers a chance to empirically identifying invariances in conceptual organization of knowledge i.e. knowledge patterns. In this paper, we present a resource of Encyclopedic Knowledge Patterns that have been discovered by analyizing the Wikipedia page links dataset, describe their evaluation with a user study, and discuss why it enables a number of research directions contributing to the realization of a meaningful Semantic Web.
Alignment-based trust for resource finding in semantic P2P networks
An Ontology Design Pattern for Referential Qualities
Referential qualities are qualities of an entity taken with reference to another entity. For example the vulnerability of a coast to sea level rise. In contrast to most non-relational qualities which only depend on their host, referential qualities require a referent additional to their host, i.e. a quality Q of an entity X taken with reference to another entity R. These qualities occur frequently in ecological systems, which make concepts from these systems challenging to model in formal ontology. In this paper, we discuss exemplary resilience, vulnerability and aﬀordance as qualities of an entity taken with reference to an external factor. We suggest an ontology design pattern for referential qualities. The design pattern is anchored in the foundational ontology DOLCE and evaluated using implementations for the notions aﬀordance, resilience and vulnerability.
Extracting Semantic User Networks From Informal Communication Exchanges
Nowadays communication exchanges are an integral and time consuming part of people’s job, especially for the so called knowledge workers. Contents discussed during meetings, instant messaging exchanges, email exchanges therefore constitute a potential source of knowledge within an organisation, which is only shared with those immediately involved in the particular communication act. This poses a knowledge management issue, as this kind of contents become “buried knowledge”. This work uses semantic technologies to extract buried knowledge, enabling expertise ﬁnding and topic trends spotting. Speciﬁcally we claim it is possible to automatically model people’s expertise by monitoring informal communication exchanges (email) and semantically annotating their content to derive dynamic user proﬁles. Proﬁles are then used to calculate similarity between people and plot semantic knowledge-based networks. The major contribution and novelty of this work is the exploitation of semantic concepts captured from informal content to build a semantic network which reﬂects people expertise rather than capturing social interactions. We validate the approach using contents from a research group internal mailing list, using email exchanges within the group collected over a ten months period.
Getting the Meaning Right: A Complementary Distributional Layer for the Web Semantics
We aim at providing a complementary layer for the web semantics, catering for bottom-up phenomena that are empirically observable on the Semantic Web rather than being merely asserted by it. We focus on meaning that is not associated with particular semantic descriptions, but emerges from the multitude of explicit and implicit links on the web of data. We claim that the current approaches are mostly top-down and thus lack a proper mechanisms for capturing the emergent aspects of the web meaning. To ﬁll this gap, we have proposed a framework based on distributional semantics (a successful bottom-up approach to meaning representation in computational linguistics) that is, however, still compatible with the top-down Semantic Web principles due to inherent support of rules. We evaluated our solution in a knowledge consolidation experiment, which conﬁrmed the promising potential of our approach.
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data
In this paper we address the problem of scalable, native and adaptive query processing over Linked Stream Data integrated with Linked Data. Linked Stream Data consists of data generated by stream sources, e.g., sensors, enriched with semantic descriptions, following the standards proposed for Linked Data. This enables the integration of stream data with Linked Data collections and facilitates a wide range of novel applications. Currently available systems use a “black box” approach which delegates the processing to other engines such as stream/event processing engines and SPARQL query processors by translating to their provided languages. As the experimental results described in this paper show, the need for query translation and data transformation, as well as the lack of full control over the query execution, pose major drawbacks in terms of efﬁciency. To remedy these drawbacks, we present CQELS (Continuous Query Evaluation over Linked Streams ), a native and adaptive query processor for uniﬁed query processing over Linked Stream Data and Linked Data. In contrast to the existing systems, CQELS uses a “white box” approach and implements the required query operators natively to avoid the overhead and limitations of closed system regimes. CQELS provides a ﬂexible query execution framework with the query processor dynamically adapting to the changes in the input data. During query execution, it continuously reorders operators according to some heuristics to achieve improved query execution in terms of delay and complexity. Moreover, external disk access on large Linked Data collections is reduced with the use of data encoding and caching of intermediate query results. To demonstrate the efﬁciency of our approach, we present extensive experimental performance evaluations in terms of query execution time, under varied query types, dataset sizes, and number of parallel queries. These results show that CQELS outperforms related approaches by orders of magnitude.
Practical RDF Schema reasoning with annotated Semantic Web data
Semantic Web data with annotations is becoming available, being YAGO knowledge base a prominent example. In this paper we present an approach to perform the closure of large RDF Schema annotated semantic web data using standard database technology. In particular, we exploit several alternatives to address the problem of computing transitive closure with real fuzzy semantic data extracted from YAGO in the PostgreSQL database management system. We benchmark the several alternatives and compare to classical RDF Schema reasoning, providing the ﬁrst implementation of annotated RDF schema in persistent storage.
Generating Resource Profiles by Exploiting the Context of Social Annotations
Typical tagging systems merely capture that part of the tagging interactions that enrich the semantics of tag assignments according to the system’s purposes. The common practice is to build tag-based resource or user proﬁles on the basis of statistics about tags, disregarding the additional evidence that pertain to the resource, the user or the tag assignment itself. Thus, the main bulk of this valuable information is ignored when generating user or resource proﬁles. In this work, we formalize the notion of tag-based and context-based resource proﬁles and introduce a generic strategy for building such proﬁles by incorporating available context information from all parts involved in a tag assignment. Our method takes into account not only the contextual information attached to the tag, the user and the resource, but also the metadata attached to the tag assignment itself. We demonstrate and evaluate our approach on two diﬀerent social tagging systems and analyze the impact of several context-based resource modeling strategies within the scope of tag recommendations. The outcomes of our study suggest a signiﬁcant improvement over other methods typically employed for this task.
ShareAlike Your Data: Self-Referential Usage Policies for the Semantic Web
Numerous forms of policies, licensing terms, and related conditions are associated with Web data and services. A natural goal for facilitating the reuse and re-combination of such content is to model usage policies as part of the data so as to enable their exchange and automated processing. This paper thus proposes a concrete policy modelling language. A particular di ﬃculty are self-referential policies such as Creative Commons ShareAlike, that mandate that derived content is published under some license with the same permissions and requirements. We present a general semantic framework for evaluating such recursive statements, show that it has desirable formal properties, and explain how it can be evaluated using existing tools. We then show that our approach is compatible with both OWL DL and Datalog, and illustrate how one can concretely model self-referential policies in these languages to obtain desired conclusions.
QueryPIE: Backward reasoning for OWL Horst over very large knowledge bases
Both materialization and backward-chaining as diﬀerent modes of performing inference have complementary advantages and disadvantages. Materialization enables very eﬃcient responses at query time, but at the cost of an expensive up front closure computation, which needs to be redone every time the knowledge base changes. Backward-chaining does not need such an expensive and change-sensitive precomputation, and is therefore suitable for more frequently changing knowledge bases, but has to perform more computation at query time. Materialization has been studied extensively in the recent semantic web literature, and is now available in industrial-strength systems. In this work, we focus instead on backward-chaining, and we present an hybrid algorithm to perform eﬃcient backward-chaining reasoning on very large datasets expressed in the OWL Horst (pD∗) fragment. As a proof of concept, we have implemented a prototype called QueryPIE (Query Parallel Inference Engine), and we have tested its performance on diﬀerent datasets of up to 1 billion triples. Our parallel implementation greatly reduces the reasoning complexity of a naive backward-chaining approach and returns results for single query-patterns in the order of milliseconds when running on a modest 8 machine cluster. To the best of our knowledge, QueryPIE is the ﬁrst reported backwardchaining reasoner for OWL Horst that eﬃciently scales to a billion triples.
Automatically Generating Data Linkages Using a Domain-Independent Candidate Selection Approach
One challenge for Linked Data is scalably establishing highquality owl:sameAs links between instances (e.g., people, geographical locations, publications, etc.) in diﬀerent data sources. Traditional approaches to this entity coreference problem do not scale because they exhaustively compare every pair of instances. In this paper, we propose a candidate selection algorithm for pruning the search space for entity coreference. We select candidate instance pairs by computing a character-level similarity on discriminating literal values that are chosen using domain-independent unsupervised learning. We index the instances on the chosen predicates’ literal values to eﬃciently look up similar instances. We evaluate our approach on two RDF and three structured datasets. We show that the traditional metrics don’t always accurately reﬂect the relative beneﬁts of candidate selection, and propose additional metrics. We show that our algorithm frequently outperforms alternatives and is able to process 1 million instances in under one hour on a single Sun Workstation. Furthermore, on the RDF datasets, we show that the entire entity coreference process scales well by applying our technique. Surprisingly, this high recall, low precision ﬁltering mechanism frequently leads to higher F-scores in the overall system.
A Novel Approach to Visualizing and Navigating Ontologies
Observational studies in the literature have highlighted low levels of user satisfaction in relation to the support for ontology visualization and exploration provided by current ontology engineering tools. These issues are particularly problematic for non-expert users, who rely on effective tool support to abstract from representational details and to be able to make sense of the contents and the structure of ontologies. To address these issues, we have developed a novel solution for visualizing and navigating ontologies, KC-Viz, which exploits an empirically-validated ontology summarization method, both to provide concise views of large ontologies, and also to support a ‘middle-out’ ontology navigation approach, starting from the most information-rich nodes (key concepts). In this paper we present the main features of KC-Viz and also discuss the encouraging results derived from a preliminary empirical evaluation, which suggest that the use of KC-Viz provides performance advantages to users tackling realistic browsing and visualization tasks. Supplementary data gathered through questionnaires also convey additional interesting findings, including evidence that prior experience in ontology engineering affects not just objective performance in ontology engineering tasks but also subjective views on the usability of ontology engineering tools.
LogMap: Logic-based and Scalable Ontology Matching
In this paper, we present LogMap—a highly scalable ontology matching system with ‘built-in’ reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for ‘on the ﬂy’ unsatisﬁability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMED CT conﬁrm that our system can eﬃciently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a ‘clean’ set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap’s output mappings with the input ontologies is consistent and does not contain unsatisﬁable classes.
Concurrent Classification of EL Ontologies
We describe an optimised consequence-based procedure for classiﬁcation of ontologies expressed in a polynomial fragment ELH R + of the OWL 2 EL proﬁle. A distinguishing property of our procedure is that it can take advantage of multiple processors/cores, which increasingly prevail in computer systems. Our solution is based on a variant of the ‘given clause’ saturation algorithm for ﬁrst-order theorem proving, where we assign derived axioms to ‘contexts’ within which they can be used and which can be processed independently. We describe an implementation of our procedure within the Java-based reasoner ELK. Our implementation is light-weight in the sense that an overhead of managing concurrent computations is minimal. This is achieved by employing lock-free data structures and operations such as ‘compare-and-swap’. We report on preliminary experimental results demonstrating a substantial speedup of ontology classiﬁcation on multi-core systems. In particular, one of the largest and widely-used medical ontologies SNOMED CT can be classiﬁed in as little as 5 seconds.
The Justificatory Structure of the NCBO BioPortal Ontologies
Current ontology development tools oﬀer debugging support by presenting justiﬁcations for entailments of OWL ontologies. While these minimal subsets have been shown to support debugging and understanding tasks, the occurrence of multiple justiﬁcations presents a significant cognitive challenge to users. In many cases even a single entailment may have many distinct justiﬁcations, and justiﬁcations for distinct entailments may be critically related. However, it is currently unknown how prevalent signiﬁcant numbers of multiple justiﬁcations per entailment are in the ﬁeld. To address this lack, we examine the justiﬁcations from an independently motivated corpus of actively used biomedical ontologies from the NCBO BioPortal. We ﬁnd that the majority of ontologies contain multiple justiﬁcations, while also exhibiting structural features (such as patterns) which can be exploited in order to reduce user eﬀort in the ontology engineering process.
Learning Relational Bayesian Classifiers from RDF Data
The increasing availability of large RDF datasets oﬀers an exciting opportunity to use such data to build predictive models using machine learning algorithms. However, the massive size and distributed nature of RDF data calls for approaches to learning from RDF data in a setting where the data can be accessed only through a query interface, e.g., the SPARQL endpoint of the RDF store. In applications where the data are subject to frequent updates, there is a need for algorithms that allow the predictive model to be incrementally updated in response to changes in the data. Furthermore, in some applications, the attributes that are relevant for speciﬁc prediction tasks are not known a priori and hence need to be discovered by the algorithm. We present an approach to learning Relational Bayesian Classiﬁers (RBCs) from RDF data that addresses such scenarios. Speciﬁcally, we show how to build RBCs from RDF data using statistical queries through the SPARQL endpoint of the RDF store. We compare the communication complexity of our algorithm with one that requires direct centralized access to the data and hence has to retrieve the entire RDF dataset from the remote location for processing. We establish the conditions under which the RBC models can be incrementally updated in response to addition or deletion of RDF data. We show how our approach can be extended to the setting where the attributes that are relevant for prediction are not known a priori, by selectively crawling the RDF data for attributes of interest. We provide open source implementation and evaluate the proposed approach on several large RDF datasets.
Concept diagrams were introduced for precisely specifying ontologies in a manner more readily accessible to developers and other stakeholders than symbolic notations. In this paper, we present a case study on the use of concept diagrams in visually specifying the Semantic Sensor Networks (SSN) ontology. The SSN ontology was originally developed by an Incubator Group of the W3C. In the ontology, a sensor is a physical object that implements sensing and an observation is observed by a single sensor. These, and other, roles and concepts are captured visually, but precisely, by concept diagrams. We consider the lessons learnt from developing this visual model and show how to convert description logic axioms into concept diagrams. We also demonstrate how to merge simple concept diagram axioms into more complex axioms, whilst ensuring that diagrams remain relatively uncluttered.
Link Prediction for Annotation Graph Datasets using Graph Summarization
Annotation graph datasets are a natural representation of scientiﬁc knowledge. They are common in the life sciences where genes or proteins are annotated with controlled vocabulary terms (CV terms) from ontologies. The W3C Linking Open Data (LOD) initiative and semantic Web technologies are playing a leading role in making such datasets widely available. Scientists can mine these datasets to discover patterns of annotation. While ontology alignment and integration across datasets has been explored in the context of the semantic Web, there is no current approach to mine such patterns in annotation graph datasets. In this paper, we propose a novel approach for link prediction; it is a preliminary task when discovering more complex patterns. Our prediction is based on a complementary methodology of graph summarization (GS) and dense subgraphs (DSG). GS can exploit and summarize knowledge captured within the ontologies and in the annotation patterns. DSG uses the ontology structure, in particular the distance between CV terms, to ﬁlter the graph, and to ﬁnd promising subgraphs. We develop a scoring function based on multiple heuristics to rank the predictions. We perform an extensive evaluation on Arabidopsis thaliana genes.
The Cognitive Complexity of OWL Justifications
In this paper, we present an approach to determining the cognitive complexity of justiﬁcations for entailments of OWL ontologies. We introduce a simple cognitive complexity model and present the results of validating that model via experiments involving OWL users. The validation is based on test data derived from a large and diverse corpus of naturally occurring justiﬁcations. Our contributions include validation for the cognitive complexity model, new insights into justiﬁcation complexity, a signiﬁcant corpus with novel analyses of justiﬁcations suitable for experimentation, and an experimental protocol suitable for model validation and reﬁnement.
Enabling fine-grained HTTP caching of SPARQL query results
As SPARQL endpoints are increasingly used to serve linked data, their ability to scale becomes crucial. Although much work has been done to improve query evaluation, little has been done to take advantage of caching. Eﬀective solutions for caching query results can improve scalability by reducing latency, network IO, and CPU overhead. We show that simple augmentation of the database indexes found in common SPARQL implementations can directly lead to eﬀective caching at the HTTP protocol level. Using tests from the Berlin SPARQL benchmark, we evaluate the potential of such caching to improve overall eﬃciency of SPARQL query evaluation.
Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter
In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial eﬀort. Users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages (tweets) and present diﬀerent methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a large-scale evaluation of faceted search strategies, show signiﬁcant improvements over keyword search and reveal signiﬁcant beneﬁts of those strategies that (i) further enrich the semantics of tweets by exploiting links posted in tweets, and that (ii) support users in selecting facet value pairs by adapting the faceted search interface to the speciﬁc needs and preferences of a user. Key words: faceted search, twitter, semantic enrichment, adaptation 1
Effectively Interpreting Keyword Queries on RDF Databases with a Rear View
Eﬀective techniques for keyword search over RDF databases incorporate an explicit interpretation phase that maps keywords in a keyword query to structured query constructs. Because of the ambiguity of keyword queries, it is often not possible to generate a unique interpretation for a keyword query. Consequently, heuristics geared toward generating the top-K likeliest user-intended interpretations have been proposed. However, heuristics currently proposed fail to capture any userdependent characteristics, but rather depend on database-dependent properties such as occurrence frequency of subgraph pattern connecting keywords. This leads to the problem of generating top-K interpretations that are not aligned with user intentions. In this paper, we propose a contextaware approach for keyword query interpretation that personalizes the interpretation process based on a user’s query context. Our approach addresses the novel problem of using a sequence of structured queries corresponding to interpretations of keyword queries in the query history as contextual information for biasing the interpretation of a new query. Experimental results presented over DBPedia dataset show that our approach outperforms the state-of-the-art technique on both eﬃciency and eﬀectiveness, particularly for ambiguous queries.
value-clustered graph functional dependency
Extending Functional Dependency to Detect Abnormal Data in RDF Graphs
Data quality issues arise in the Semantic Web because data is created by diverse people and/or automated tools. In particular, erroneous triples may occur due to factual errors in the original data source, the acquisition tools employed, misuse of ontologies, or errors in ontology alignment. We propose that the degree to which a triple deviates from similar triples can be an important heuristic for identifying errors. Inspired by functional dependency, which has shown promise in database data quality research, we introduce value-clustered graph functional dependency to detect abnormal data in RDF graphs. To better deal with Semantic Web data, this extends the concept of functional dependency on several aspects. First, there is the issue of scale, since we must consider the whole data schema instead of being restricted to one database relation. Second, it deals with multi-valued properties without explicit value correlations as speciﬁed as tuples in databases. Third, it uses clustering to consider classes of values. Focusing on these characteristics, we propose a number of heuristics and algorithms to eﬃciently discover the extended dependencies and use them to detect abnormal data. Experiments have shown that the system is eﬃcient on multiple data sets and also detects many quality problems in real world data.
Effective and Efficient Entity Search in RDF data
Triple stores have long provided RDF storage as well as data access using expressive, formal query languages such as SPARQL. The new end users of the Semantic Web, however, are mostly unaware of SPARQL and overwhelmingly prefer imprecise, informal keyword queries for searching over data. At the same time, the amount of data on the Semantic Web is approaching the limits of the architectures that provide support for the full expressivity of SPARQL. These factors combined have led to an increased interest in semantic search, i.e. access to RDF data using Information Retrieval methods. In this work, we propose a method for eﬀective and eﬃcient entity search over RDF data. We describe an adaptation of the BM25F ranking function for RDF data, and demonstrate that it outperforms other state-of-the-art methods in ranking RDF resources. We also propose a set of new index structures for eﬃcient retrieval and ranking of results. We implement these results using the open-source MG4J framework.
Large Scale Fuzzy pD* Reasoning using Map Reduce
The MapReduce framework has proved to be very eﬃcient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD ∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic data under fuzzy pD ∗ semantics (i.e., an extension of OWL pD ∗ semantics with fuzzy vagueness). To the best of our knowledge, this is the ﬁrst work to investigate how MapReduce can help to solve the scalability issue of fuzzy OWL reasoning. While most of the optimizations used by the existing MapReduce framework for pD ∗ semantics are also applicable for fuzzy pD ∗ semantics, unique challenges arise when we handle the fuzzy information. We identify these key challenges, and propose a solution for tackling each of them. Furthermore, we implement a prototype system for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD ∗ semantics.
DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data
Triple stores are the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations. In this paper, we propose a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triple stores and, thus, settled on measuring performance against a relational database which had been converted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more useful to compare existing triple stores and provide results for the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the performance of triple stores is by far less homogeneous than suggested by previous benchmarks. 1
Ontology veriﬁcation is concerned with the relationship between the intended structures for an ontology and the models of the axiomatization of the ontology. The veriﬁcation of a particular ontology requires characterization of the models of the ontology up to isomorphism and a proof that these models are equivalent to the intended structures for the ontology. In this paper we provide the veriﬁcation of the ontology of time introduced by Hobbs and Pan, which is a ﬁrst-order axiomatization of OWL-Time. We identify ﬁve modules within this ontology and present a complete account of the metatheoretic relationships among the modules and between other time ontologies for points and intervals.
ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints
Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, ﬁxed plans identiﬁed following the traditional optimize-thenexecute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data tra ﬃc is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.
An Empirical Study of Vocabulary Relatedness and Its Application to Recommender Systems
Semantic Search: Reconciling Expressive Querying and Exploratory Search
Faceted search and querying are two well-known paradigms to search the Semantic Web. Querying languages, such as SPARQL, offer expressive means for searching RDF datasets, but they are diﬃcult to use. Query assistants help users to write well-formed queries, but they do not prevent empty results. Faceted search supports exploratory search, i.e., guided navigation that returns rich feedbacks to users, and prevents them to fall in dead-ends (empty results). However, faceted search systems do not oﬀer the same expressiveness as query languages. We introduce Query-based Faceted Search (QFS), the combination of an expressive query language and faceted search, to reconcile the two paradigms. In this paper, the LISQL query language generalizes existing semantic faceted search systems, and covers most features of SPARQL. A prototype, Sewelis (aka. Camelis 2), has been implemented, and a usability evaluation demonstrated that QFS retains the ease-of-use of faceted search, and enables users to build complex queries with little training.
Wheat and Chaff -- Practically Feasible Interactive Ontology Revision
When ontological knowledge is acquired automatically, quality control is essential. We consider the tightest possible approach – an exhaustive manual inspection of the acquired data. By using automated reasoning, we partially automate the process: after each expert decision, axioms that are entailed by the already approved statements are automatically approved, whereas axioms that would lead to an inconsistency are declined. Adequate axiom ranking strategies are essential in this setting to minimize the amount of expert decisions. In this paper, we present a generalization of the previously proposed ranking techniques which works well for arbitrary validity ratios – the proportion of valid statements within a dataset – whereas the previously described ranking functions were either tailored towards validity ratios of exactly 100% and 0% or were optimizing the worst case. The validity ratio – generally not known a priori – is continuously estimated over the course of the inspection process. We further employ partitioning techniques to signiﬁcantly reduce the computational e ﬀort. We provide an implementation supporting all these optimizations as well as featuring a user front-end for successive axiom evaluation, thereby making our proposed strategy applicable to practical scenarios. This is witnessed by our evaluation showing that the novel parameterized ranking function almost achieves the maximum possible automation and that the computation time needed for each reasoning-based, automatic decision is reduced to less than one second on average for our test dataset of over 25,000 statements.
FedBench: A Benchmark Suite for Federated Semantic Data Query Processing
In this paper we present FedBench, a comprehensive benchmark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face diﬀerent settings at both the data and query level, such as varying data access interfaces, incomplete knowledge about data sources, availability of diﬀerent statistics, and varying degrees of query expressiveness. Accounting for this heterogeneity, we present a highly ﬂexible benchmark suite, which can be customized to accommodate a variety of use cases and compare competing approaches. We discuss design decisions, highlight the ﬂexibility in customization, and elaborate on the choice of data and query sets. The practicability of our benchmark is demonstrated by a rigorous evaluation of various application scenarios, where we indicate both the beneﬁts as well as limitations of the state-of-the-art federated query processing strategies for semantic data.
Entities on the Web of Data need to have labels in order to be exposable to humans in a meaningful way. These labels can then be used for exploring the data, i.e., for displaying the entities in a linked data browser or other front-end applications, but also to support keywordbased or natural-language based search over the Web of Data. Far too many applications fall back to exposing the URIs of the entities to the user in the absence of more easily understandable representations such as human-readable labels. In this work we introduce a number of labelrelated metrics: completeness of the labeling, the eﬃcient accessibility of the labels, unambiguity of labeling, and the multilinguality of the labeling. We report our ﬁndings from measuring the Web of Data using these metrics. We also investigate which properties are used for labeling purposes, since many vocabularies deﬁne further labeling properties beyond the standard property from RDFS.
Extending Logic Programs with Description Logic Expressions for the Semantic Web
Recently much attention has been directed to extending logic programming with description logic (DL) expressions, so that logic programs have access to DL knowledge bases and thus are able to reason with ontologies in the Semantic Web. In this paper, we propose a new extension of logic programs with DL expressions, called normal DL logic programs. In a normal DL logic program arbitrary DL expressions are allowed to appear in rule bodies and atomic DL expressions (i.e., atomic concepts and atomic roles) allowed in rule heads. We extend the key condition of well-supportedness for normal logic programs under the standard answer set semantics to normal DL logic programs and deﬁne an answer set semantics for DL logic programs which satisﬁes the extended well-supportedness condition. We show that the answer set semantics for normal DL logic programs is decidable if the underlying description logic is decidable (e.g. SHOIN or SROIQ).
RELIN: Relatedness and Informativeness-based Centrality for Entity Summarization
Linked Data is developing towards a large, global repository for structured, interlinked descriptions of real-world entities. An emerging problem in many Web applications making use of data like Linked Data is how a lengthy description can be tailored to the task of quickly identifying the underlying entity. As a solution to this novel problem of entity summarization, we propose RELIN, a variant of the random surfer model that leverages the relatedness and informativeness of description elements for ranking. We present an implementation of this conceptual model, which captures the semantics of description elements based on linguistic and information theory concepts. In experiments involving real-world data sets and users, our approach outperforms the baselines, producing summaries that better match handcrafted ones and further, shown to be useful in a concrete task.
In this paper, we study watermarking methods to prove the ownership of an ontology. Diﬀerent from existing approaches, we propose to watermark not by altering existing statements, but by removing them. Thereby, our approach does not introduce false statements into the ontology. We show how ownership of ontologies can be established with provably tight probability bounds, even if only parts of the ontology are being re-used. We ﬁnally demonstrate the viability of our approach on real-world ontologies.
Querying OWL 2 QL and Non-monotonic Rules
Answering (conjunctive) queries is an important reasoning task in Description Logics (DL), hence also in highly expressive ontology languages, such as OWL. Extending such ontology languages with rules, such as those expressible in RIF-Core, and further with non-monotonic rules, integrating default negation as described in the RIF-FLD, yields an even more expressive language that allows for modeling defaults, exceptions, and integrity constraints. Here, we present a top-down procedure for querying knowledge bases (KB) that combine non-monotonic rules with an ontology in DL-Lite R – the DL underlying the OWL 2 proﬁle OWL 2 QL. This proﬁle aims particularly at answering queries in an eﬃcient way for KB with large ABoxes. Our procedure extends the query-answering facility to KB that also include non-monotonic rules, while maintaining tractability of reasoning (w.r.t. data complexity). We show that the answers are sound and complete w.r.t. the well-founded MKNF model for hybrid MKNF KB K.
Analysis and evaluation of usability and uptake of Semantic
Description of implementation and use of semantic
Wiki-based conceptual modeling: an experience with the Public Administration
The dematerialization of documents produced within the Public Administration (PA) represents a key contribution that ICT can provide towards the modernization of services within the PA. The availability of proper and precise models of the administrative procedures, and of the specific ``entities'' related to these procedures, such as the documents involved in the procedures or the organizational roles performing the activities, is an important step towards both (1) the replacement of paper-based procedures with electronic-based ones, and (2) the definition of guidelines and functions needed to safely store, catalogue, manage and retrieve in an appropriate archival system the electronic documents produced within the PA . In this paper we report the experience of customizing a semantic wiki based tool (MoKi) for the modeling of administrative procedures (processes) and their related ``entities'' (ontologies). The tool has been used and evaluated by several domain experts from different Italian regions in the context of a national project. This experience, and the reported evaluation, highlight the potential and criticality of using semantic wiki-based tools for the modeling of complex domains composed of processes and ontologies in a real setting.
The dematerialization of documents produced within the Public Administration (PA) represents a key contribution that Information and Communication Technology can provide towards the modernization of services within the PA. The availability of proper and precise models of the administrative procedures, and of the speciﬁc “entities” related to these procedures, such as the documents involved in the procedures or the organizational roles performing the activities, is an important step towards both (1) the replacement of paper-based procedures with electronic-based ones, and (2) the deﬁnition of guidelines and functions needed to safely store, catalogue, manage and retrieve in an appropriate archival system the electronic documents produced within the PA. In this paper we report the experience of customizing a semantic wiki based tool (MoKi) for the modeling of administrative procedures (processes) and their related “entities” (ontologies). The tool has been used and evaluated by several domain experts from different Italian regions in the context of a national project. This experience, and the reported evaluation, highlight the potential and criticality of using semantic wiki-based tools for the modeling of complex domains composed of processes and ontologies in a real setting.
SCMS - Semantifying Content Management Systems
The migration from the document-oriented to the Semantic Web requires from CMS that they integrate human- and machine-readable data to support their seamless integration into the Semantic Web. Yet, there is still a blatant need for frameworks that can be easily integrated into CMS and allow to transform their content into machine-readable knowledge with high accuracy. In this paper, we describe the SCMS framework, whose main goal is to allow for the accurate extraction of knowledge from any CMS and the integration of this knowledge into the same CMS. Our framework integrates a highly accurate knowledge extraction pipeline. In addition, it relies on RDF and HTTP standards and can thus be integrated in virtually any CMS. We present how our framework is being used in the energy sector. We also evaluate our approach and show that our framework reaches up to 96% F-score, therewith outperforming even state-of-the-art commercial software.
The migration to the Semantic Web requires from CMS that they integrate humanand machine-readable data to support their seamless integration into the Semantic Web. Yet, there is still a blatant need for frameworks that can be easily integrated into CMS and allow to transform their content into machine-readable knowledge with high accuracy. In this paper, we describe the SCMS (Semantic Content Management Systems) framework, whose main goals are the extraction of knowledge from unstructured data in any CMS and the integration of the extracted knowledge into the same CMS. Our framework integrates a highly accurate knowledge extraction pipeline. In addition, it relies on the RDF and HTTP standards for communication and can thus be integrated in virtually any CMS. We present how our framework is being used in the energy sector. We also evaluate our approach and show that our framework outperforms even commercial software by reaching up to 96% F-score.
Leveraging Community-built Knowledge for Type Coercion in Question Answering
Watson, the winner of the Jeopardy! challenge, is a state-of-the-art open-domain Question Answering system that tackles the fundamental issue of answer typing by using a novel type coercion (TyCor) framework. Candidate answers are initially produced without considering type information, and subsequent stages check whether the candidate can be coerced into the expected answer type. In this paper, we provide a high-level overview of the TyCor framework and discuss how it is integrated in Watson, focusing on and evaluating three TyCor components that leverage the community built semi-structured and structured knowledge resources DBpedia (in conjunction with the YAGO ontology), Wikipedia Categories and Lists. These resources complement each other well in terms of precision and granularity of type information, and through links to Wikipedia, provide coverage for a large set of instances.
Rule-based OWL Reasoning for specific Embedded Devices
Ontologies have been used for formal representation of knowledge for many years now. One possible knowledge representation language for ontologies is the OWL 2 Web Ontology Language, informally OWL2. The OWL specification includes the definition of variants of OWL, with different levels of expressiveness. OWL DL and OWL Lite are based on Description Logics, for which sound, complete, and terminating reasoners exit. Unfortunately, all these reasoners are too complex for embedded systems. But since evaluation of ontologies on these resource constrained devices becomes more and more necessary (e.g. for diagnostics) we developed a OWL reasoner for embedded devices. We use the OWL 2 sublanguage OWL 2 RL, which can be completely implemented using rule-based reasoning engines. In this paper we present the used embedded hardware, the implemented reasoning component, and results regarding performance and memory consumption.
Ontologies have been used for formal representation of knowledge for many years now. One possible knowledge representation language for ontologies is the OWL 2 Web Ontology Language, informally OWL 2. The OWL speciﬁcation includes the deﬁnition of variants of OWL, with different levels of expressiveness. OWL DL and OWL Lite are based on Description Logics, for which sound and complete reasoners exits. Unfortunately, all these reasoners are too complex for embedded systems. But since evaluation of ontologies on these resource constrained devices becomes more and more necessary (e.g. for diagnostics) we developed an OWL reasoner for embedded devices. We use the OWL 2 sub language OWL 2 RL, which can be implemented using rule-based reasoning engines. In this paper we present our used embedded hardware, the implemented reasoning component, and results regarding performance and memory consumption.
Mind Your Metadata: Exploiting Semantics for Configuration, Adaptation, and Provenance in Scientific Workflows
Scientific metadata containing semantic descriptions of scientific data is expensive to capture and is typically not used across entire data analytic processes. We present an approach where semantic metadata is generated as scientific data is being prepared, and then subsequently used to configure models and to customize them to the data. The metadata captured includes sensor descriptions, data characteristics, data types, and process documentation. This metadata is then used in a workflow system to select analytic models dynamically and to set up model parameters automatically. In addition, all aspects of data processing are documented, and the system is able to generate extensive provenance records for new data products based on the metadata. As a result, the system can dynamically select analytic models based on the metadata properties of the data it is processing, generating more accurate results. We show results in analyzing stream metabolism for watershed ecosystem management.
Scientific metadata containing semantic descriptions of scientific data is expensive to capture and is typically not used across entire data analytic processes. We present an approach where semantic metadata is generated as scientific data is being prepared, and then subsequently used to configure models and customize them to the data. The metadata captured includes sensor descriptions, data characteristics, data types, and process documentation. This metadata is then used in a workflow system to select analytic models dynamically and to set up model parameters automatically. In this architecture, all aspects of data processing are documented, and the system is able to generate extensive provenance records for new data products. As a result, the system can dynamically select analytic models based on the metadata properties of the data it is processing, generating more accurate results. We show results in analyzing stream metabolism for watershed ecosystem management.
Using Semantic Web Technologies to Build a Community-driven Knowledge Curation Platform for the Skeletal Dysplasia Domain
In this paper we report on our on-going efforts in building SKELETOME - a community-driven knowledge curation platform for the skeletal dysplasia domain. SKELETOME introduces an ontology-driven knowledge engineering cycle that supports the continuous evolution of the domain knowledge. Newly submitted, undiagnosed patient cases undergo a collaborative diagnosis process that transforms them into well-structured case studies, classified, linked and discoverable based on their likely diagnosis(es). The paper presents the community requirements driving the design of the platform, the underlying implementation details and the results of a preliminary usability study. Because SKELETOME is built on Drupal 7, we discuss the limitations of some of its embedded Semantic Web components and describe a set of new modules, developed to handle these limitations (which will soon be released as open source to the community).
In this paper we report on our on-going eﬀorts in building SKELETOME – a community-driven knowledge curation platform for the skeletal dysplasia domain. SKELETOME introduces an ontologydriven knowledge engineering cycle that supports the continuous evolution of the domain knowledge. Newly submitted, undiagnosed patient cases undergo a collaborative diagnosis process that transforms them into well-structured case studies, classiﬁed, linked and discoverable based on their likely diagnosis(es). The paper presents the community requirements driving the design of the platform, the underlying implementation details and the results of a preliminary usability study. Because SKELETOME is built on Drupal 7, we discuss the limitations of some of its embedded Semantic Web components and describe a set of new modules, developed to handle these limitations (which will soon be released as open source to the community).
An Implementation of a Semantic, Web-Based Virtual Machine Laboratory Prototyping Environment
Creation of virtual machine laboratories – simulated planning and learning environments demonstrating function and structure of working machines – often involve a lot of manual labor. A notable source of the labor is the programming required due to changes in structural and functional models of a system. As a result, rapid prototyping of a virtual machine laboratory becomes difficult, if not impossible. We argue that by using a combination of semantic modeling and prototyping with a web-based system, more rapid development of virtual machine laboratories can be achieved. In this paper, we present the design and implementation of a semantic, web-based virtual machine laboratory prototyping environment. Application of the environment to a case example is also described and discussed.
Creation of virtual machine laboratories – simulated planning and learning environments demonstrating function and structure of working machines – often involves a lot of manual labor. A notable source of labor is the programming required due to changes in structural and functional models of a system. As a result, rapid prototyping of a virtual machine laboratory often becomes more difficult if not impossible. We argue that using a combination of semantic modeling and a web-based prototyping system, more rapid virtual machine laboratory prototyping can be achieved. In this paper, we present the design and implementation of a semantic, web-based virtual machine laboratory prototyping environment. The environment is evaluated in prototyping test cases.
The Cyber Scientific Method (CSM) formalizes experimentation on computer systems, hardware, software, and networks on the U.S. National Cyber Range. This formalism provides rigor to cyber tests to ensure knowledge can be shared and experiment results can be viewed with confidence, knowing exactly what was tested under what conditions. Cyber Scientific Test Language (CSTL) is an ontology-based language for CSM experiments. CSTL describes test objectives, statistical experiment design, test network composition, sensor placement, and data analysis and visualization. CSTL represents CSM experiments throughout their lifecycle, from test design through detailed test network description, instrumentation and control network augmentation, testbed buildout, data collection, and analysis. The representation of this information in a formal ontology has several benefits. It enables use of general-purpose reasoners to query and recombine test specifications for rapidly building an experiment network and testbed on the range. Additionally, it facilitates knowledge management and retrieval of test procedures and results.
The Cyber Scientific Method (CSM) is a formalism for experimentation on computer systems, hardware, software, and networks on the National Cyber Range. This formalism provides rigor to cyber tests to ensure that knowledge can be shared and experiment results can be viewed with confidence, knowing exactly what was tested under what conditions. The Cyber Scientific Test Language (CSTL) is an ontology-based language to describe CSM experiments. CSTL descriptions encompass test objectives, statistical experiment design, test network composition, sensor placement, and data analysis and visualization. CSTL expresses information about CSM experiments throughout their lifecycle, from initial test design through detailed test network description, instrumentation and control network augmentation, testbed buildout, data collection, and final analysis. The detailed representation of this information in a formal ontology has several benefits. It supports the use of general-purpose reasoners to query and recombine test specifications to support rapidly building an experiment network and testbed on the range. Additionally, it facilitates knowledge management and retrieval of test procedures and results. Test specifications can be viewed either with custom tools or with general-purpose applications. This paper provides an overview of CSTL and how it is used throughout the cyber test design, execution, analysis, and review process.
KOIOS: Utilizing Semantic Search for Easy-Access and Visualization of Structured Environmental Data
With the increasing interest in environmental issues, the amount of publicly available environmental data on the Web is continuously growing. Despite its importance, the uptake of environmental information by the ordinary Web users is still very limited due to intransparent access to complex and distributed databases. As a remedy to this problem, in this work, we propose the use of semantic search technologies recently developed as an intuitive way to easily access structured data and lower the barriers to obtain information satisfying users' information need. Our proposed system, namely KOIOS, enables a simple, keyword-based search on structured environmental data and built on top of a commercial Environmental Information System (EIS). A prototype system implemented successfully shows the intuitive means to facilitate search and access to complex environmental information by applying semantic search techniques.
With the increasing interest in environmental issues, the amount of publicly available environmental data on the Web is continuously growing. Despite its importance, the uptake of environmental information by the ordinary Web users is still very limited due to intransparent access to complex and distributed databases. As a remedy to this problem, in this work, we propose the use of semantic search technologies recently developed as an intuitive way to easily access structured data and lower the barriers to obtain information satisfying user information needs. Our proposed system, namely KOIOS, enables a simple, keyword-based search on structured environmental data and built on top of a commercial Environmental Information System (EIS). A prototype system successfully shows that applying semantic search techniques this way provides intuitive means for search and access to complex environmental information.
The MetaLex Document Server - Legal Documents as Versioned Linked Data
This paper introduces the MetaLex Document Server (MDS), an ongoing project to improve access to legal sources (regulations, court rulings) by means of a generic legal XML syntax (CEN MetaLex) and Linked Data. The MDS defines a generic conversion mechanism from legacy legal XML syntaxes to CEN MetaLex, RDF and Pajek network files, and discloses content by means of HTTP-based content negotiation, a SPARQL endpoint and a basic search interface. MDS combines a trans- parent (versioned) and opaque (content-based) naming scheme for URIs of parts of legal texts, allowing for tracking of version information at the URI-level, as well as reverse engineering of versioned metadata from sources that provide only partial information, such as many web-based legal content services. The MDS hosts all current 27 thousand national regulations of the Netherlands, comprising some 87.9M triples.
This paper introduces the MetaLex Document Server (MDS), an ongoing project to improve access to legal sources (regulations, court rulings) by means of a generic legal XML syntax (CEN MetaLex) and Linked Data. The MDS deﬁnes a generic conversion mechanism from legacy legal XML syntaxes to CEN MetaLex, RDF and Pajek network ﬁles, and discloses content by means of HTTP-based content negotiation, a SPARQL endpoint and a basic search interface. MDS combines a transparent (versioned) and opaque (content-based) naming scheme for URIs of parts of legal texts, allowing for tracking of version information at the URI-level, as well as reverse engineering of versioned metadata from sources that provide only partial information, such as many web-based legal content services. The MDS hosts all 28k national regulations of the Netherlands available since May 2011, comprising some 100M triples.
BookSampo - Lessons Learned in Creating a Semantic Portal for Fiction Literature
BookSampo is a semantic portal in use, covering metadata about practically all Finnish fiction literature of Finnish public libraries on a work level. The system introduces a variety of semantic web novel- ties deployed into practise: The underlying data model is based on the emerging functional, content-centered metadata indexing paradigm using RDF. Linked Data (LD) principles are used for mapping the metadata with tens of interlinked ontologies in the national FinnONTO ontology infrastructure. The contents are also linked with the large LD metadata repository of related cultural heritage content of CultureSampo. Book- Sampo is actually based on using CultureSampo as a semantic web ser- vice, demonstrating the idea of re-using semantic content from multiple perspectives without the need for modifications. Most of the content has been transformed automatically from existing databases, with the help of ontologies derived from thesauri in use in Finland, but in addtion tens of volunteered librarians have participated in a Web 2.0 fashion in anno- tating and correcting the metadata, especially regarding older litarature. For this purpose, semantic web editing tools and public ONKI ontology services were created and used. The paper focuses on lessons learned in the process of creating the semantic web basis of BookSampo.
BookSampo is a semantic portal in use, covering metadata about practically all Finnish ﬁction literature of Finnish public libraries on a work level. The system introduces a variety of semantic web novelties deployed into practise: The underlying data model is based on the emerging functional, content-centered metadata indexing paradigm using RDF. Linked Data principles are used for mapping the metadata with tens of interlinked ontologies in the national FinnONTO ontology infrastructure. The contents are also linked with the large Linked Data repository of related cultural heritage content of CultureSampo. BookSampo is actually based on using CultureSampo as a semantic web service, demonstrating the idea of re-using semantic content from multiple perspectives without the need for modiﬁcations. Most of the content has been transformed automatically from existing databases, with the help of ontologies derived from thesauri in use in Finland, but in addition tens of volunteered librarians have participated in a Web 2.0 fashion in annotating and correcting the metadata, especially regarding older litarature. For this purpose, semantic web editing tools and public ONKI ontology services were created and used. The paper focuses on lessons learned in the process of creating the semantic web basis of BookSampo.
Privacy-Aware and Scalable Content Dissemination in Distributed Social Networks
Centralized social networking websites raise scalability issues — due to the growing number of participants — and, as well as, policy concerns — such as control, privacy and ownership over the user’s published data. Distributed Social Networks aim to solve this issue by enabling architecture where people own their data and share it their own way. However, the privacy and scalability challenge is still to be tackled. This paper presents a privacy-aware extension to Google’s PubSubHubbub protocol, using Semantic Web technologies, solving both the scalability and the privacy issues in Distributed Social Networks. We enhanced the traditional feature of PubSubHubbub protocol — that decouples publishers and subscribers — in order to allow publishers to decide whom they want to share their information with. We also present a use-case of applying this to SMOB (our (Semantic Microblogging framework). Our architecture is application agnostic, and can be adopted by any system that requires scalable and privacy-aware content broadcasting.
Zhishi.me - Weaving Chinese Linking Open Data
Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia. Finally, we also introduce three Web access entries namely SPARQL endpoint, lookup interface and detailed data view, which conform to the principles of publishing data sources to LOD.
How to "Make a Bridge to the new Town" using OntoAccess
Business-critical legacy applications often rely on relational databases to sustain daily operations. Introducing Semantic Web technology in newly developed systems is often difficult, as these systems need to run in tandem with their predecessors and cooperatively read and update existing data. A common pattern is to incrementally migrate data from a legacy system to its successor by running the new system in parallel, with a data bridge in between. Existing approaches that can deployed as a data bridge in theory, restrict Semantic Web-enabled applications to read legacy data in practice, disallowing update operations completely. This paper explains how our RDB-to-RDF tool OntoAccess can be used to transition legacy systems into Semantic Web-enabled applications. By means of a case study, we exemplify how we successfully made a bridge between one of our own large-scale legacy applications and its long-term replacement. We elaborate on challenges we faced during the migration process and how we were able to overcome them.
Linking Semantic Desktop Data to the Web of Data
The Semantic Desktop has enabled better organization of the personal information on our computers, by applying semantic technologies on the desktop. However, information on our desktop is often incomplete, as it is based on our subjective view, or limited knowledge about an entity. On the other hand, the Web of Data contains information about virtually everything, generated by multiple sources. Connecting the desktop to the Web of Data would thus enrich and complement desktop information. Bringing in information from the Web of Data automatically would take the burden of searching for information off the user. In addition, connecting the two networks of data opens up the possibility of advanced personal services on the desktop. Our solution tackles the problems raised above by using a semantic search engine for the Web of Data, such as Sindice, to find and retrieve a relevant subset of entities from the Web. We then present a matching framework, using a combination of configurable heuristics and rules to compare data graphs, that achieves a high degree of precision in the linking decision. We evaluate our methodology with real-world data. We create a gold standard from relevance judgements by experts, and we measure the performance of our system against it. We show that it is possible to automatically link desktop data with web data in a effective way.
The goal of the Semantic Desktop is to enable better organization of the personal information on our computers, by applying semantic technologies on the desktop. However, information on our desktop is often incomplete, as it is based on our subjective view, or limited knowledge about an entity. On the other hand, the Web of Data contains information about virtually everything, generally from multiple sources. Connecting the desktop to the Web of Data would thus enrich and complement desktop information. Bringing in information from the Web of Data automatically would take the burden of searching for information oﬀ the user. In addition, connecting the two networks of data opens up the possibility of advanced personal services on the desktop. Our solution tackles the problems raised above by using a semantic search engine for the Web of Data, such as Sindice, to ﬁnd and retrieve a relevant subset of entities from the web. We present a matching framework, using a combination of conﬁgurable heuristics and rules to compare data graphs, that achieves a high degree of precision in the linking decision. We evaluate our methodology with real-world data; create a gold standard from relevance judgements by experts, and we measure the performance of our system against it. We show that it is possible to automatically link desktop data with web data in an eﬀective way.
Pragmatic Considerations for Semantic Environmental
A Semantic Portal for Next Generation Monitoring Systems
We present a semantic technology-based approach to emerging monitoring systems based on our linked data approach in the Tetherless World Constellation Semantic Ecology and Environment Portal (SemantEco). Our integration scheme uses an upper level monitoring ontology and mid-level monitoring-relevant domain ontologies. The initial domain ontologies focus on water and air quality. We then integrate domain data from different authoritative sources and multiple regulation ontologies (capturing federal as well as state guidelines) to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach captures and leverages provenance to enable transparency. In addition, SemantEco features provenance-based facet generation, query answering, and validation over the integrated data via SPARQL. We introduce the general SemantEco approach, describe the implementation which has been built out substantially in the water domain creating the SemantAqua portal, and highlight some of the potential impacts for the future of semantically-enabled monitoring systems.
Linking Data Across Universities: an integrated video lectures dataset
This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scrapped and structured according to well-known vocabularies, like FOAF1, or the W3C Ontology for Media Resources2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14.000 video lectures from 27 different institutions and categorized under a common classification scheme.
This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1 , or the W3C Ontology for Media Resources 2 . To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3 . An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme.
Debugging ontologies and mappings in ontology networks
Developing ontologies is not an easy task and, as the ontologies grow in size, they are likely to show a number of defects. Such ontologies, although often useful, also lead to problems when used in semantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. Defects in ontologies can take different forms. Syntactic defects are usually easy to find and to resolve. Defects regarding style include such things as unintended redundancy. More interesting and severe defects are the modeling defects which require domain knowledge to detect and resolve such as defects in the structure, and semantic defects such as unsatisfiable concepts and inconsistent ontologies. Further, during the recent years more and more mappings between ontologies with overlapping information have been generated, e.g. using ontology alignment systems, thereby connecting the ontologies in ontology networks. This has led to a new opportunity to deal with defects as the mappings and other ontologies in the network may be used in the debugging of a particular ontology in the network. It also has introduced a new difficulty as the mappings may not always be correct and need to be debugged themselves. In this tutorial we give an overview of current approaches for debugging semantic and structure defects in ontologies (single or in an ontology network) as well as approaches for debugging mappings. Tutorial webpage: http://www.ida.liu.se/~patla/courses/ISWC11/
This tutorial will cover the code of the SPARQL query language then describe how the SPARQL algebra defines the semantics of query execution. It will give the participant a solid understanding of how to write and design SPARQL queries, from basic access through to use of the newest features as well as a grounding in the formal underpinnings of the language.
Watson (http://ibmwatson.com) is an open-domain natural language question answering system that answers questions with precision and confidence rivaling the best human experts at the task, and that emerged victorious in a widely viewed public challenge on the American TV Quiz Show, Jeopardy!. Semantic Web Technology, enhanced by a massive use of open linked data, plays a crucial role in Watson. For example, linked data such as DBpedia and Geonames and triple stores such as Sesame have been used to generate candidate answers and to score them under multiple points of view such as type coercion and geographic proximity. In addition, the connection between linked data and natural language text offered by Wikipedia has been very useful to generate open domain training data for relation detection and entity recognition systems, substantially improving the NLP capabilities of the system and therefore allowing the development of a truly open domain QA system. This tutorial is focus on the Semantic Web Technology adopted by Watson and on how it fits in the general Deep QA architecture. The tutorial is structured in two parts, the first focusing on the open-domain QA challenge and the Watson architecture, the second focused on the use of semantic web technology and linked open data in Watson. Tutorial webpage: http://iswc2011.semanticweb.org/tutorials/semantic-web-technology-in-watson/
Scalable Integration and Processing of Linked Data
Given the recent growth in openly-available, rich, structured information published on the Web as Linked Data, this tutorial series focuses on consuming large corpora of such data, with particular emphasis on techniques for performing scalable integration. Following a brief introduction to Linked Data principles and publishing, we cover techniques for crawling, indexing, querying and reasoning over such data. We also highlight pragmatic issues faced when performing such tasks. Along these lines, the Scalable Integration of Linked Data tutorial series targets practitioners who wish to leverage existing Linked Data for their applications, but is also directly relevant to a wider audience hoping to learn more about Linked Data and the related standards. Tutorial webpage: http://sild.cs.vu.nl/
Ten rules to make your semantic app addictive – revisited
In many application scenarios useful semantic content can hardly be created (fully) automatically, but motivating people to become an active part of this endeavor is still an art more than a science. In this tutorial we will look into fundamental design issues of semantic-content authoring technology and of the applications deploying such technology in order to find out which incentives speak to people to become engaged with the Semantic Web, and to determine the ways these incentives can be transferred into technology design. We will present how methods and techniques from areas as diverse as participation management, usability engineering, mechanism design, social computing, and game mechanics can be jointly applied to analyze semantically enabled applications, and subsequently design incentives-compatible variants thereof. The discussion will be framed by three case studies on the topics of enterprise knowledge management, media and entertainment, and IT ecosystems, in which combinations of these methods and techniques has led to increased user participation in creating useful semantic descriptions of various types of digital resources text documents, images, videos and Web services and APIs. Furthermore, we will revisit the best practices and guidelines that have been at the core of an earlier version of this tutorial at the previous edition of the ISWC in 2010, following the empirical findings and insights gained during the operation of the three case studies just mentioned. These guidelines provide IT developers with a baseline to create technology and end-user applications that are not just functional, but facilitate and encourage user participation that supports the further development of the Semantic Web. Tutorial webpage: http://www.insemtives.eu/iswc2011-tutorial/
The GoodRelations ontology (http://purl.org/goodrelations/) is one huge success story of applying Semantic Web technology to business challenges. In this tutorial, we will (1) give a comprehensive overview and hands-on training on the conceptual structures of the GoodRelations ontology including patterns for ownership and demand, (2) present the full tool chain for producing and consuming GoodRelations-related data, (3) explain the long-term vision of linked open commerce, (4) describe the main challenges for future research in the field, and (5) discuss advanced topics, like access control, identity and authentication (e.g. with WebID); micropayment services (like Payswarm), and data management issues from the publisher and consumer perspective. Tutorial webpage: http://wiki.goodrelations-vocabulary.org/Events/ISWC2011
But I did a user study! vs "What do you mean I need a user study?"
Hi there. Does the following sound like you? You are creating a tool that is supposedly for Real People to use, so you figure, do a user study of some kind and you'll submit it as a paper? You've tried the above and the paper didn't get accepted saying "this isn't research; it's just applied user centered design" You want to validate your tool work with users but you don't have the expertise either to test your idea or prove that it's a user interaction contribution? You've designed a tool that's a really cool technology whether users use it or not, you don't care about the user testing, you just want to get the contribution out there, but reviewers say "where's the evaluation"? If any of the above scenarios sound like you, we feel your pain. But don't worry: we can help. Tutorial webpage: http://iswc2011.semanticweb.org/tutorials/but-i-did-a-user-study-vs-what-do-you-mean-i-need-a-user-study/
PC Member at ISWC2011 (Doctoral Consortium)
PC Member at ISWC2011 (Poster and Demonstration Track)
PC Member at ISWC2011 (Semantic Web in Use)
Lecture Notes in Computer Science (LNCS)
Proceedings of the 10th International Semantic Web Conference, ISWC2011, Bonn, Germany, 23 - 27 October, 2011
Senior PC Member at ISWC2011 (Research Track)
This session will further familiarize participants to the SciVerse Applications platform, an OpenSocial based gadgets framework which extends the online search and discovery platform SciVerse. Using Elsevier Content and Framework APIs, developers can create apps to integrate data and tools into the scientific articles on ScienceDirect and Scopus, and complement the search workflows across all of SciVerse. SciVerse contains about a quarter of the worlds peer reviewed scientific full text articles (currently numbering at over 11 million) as well as the world's largest abstract and citation database containing 40 million abstracts, plus hundreds of millions of scientific web documents, author profiles and affiliation profiles. Combining search and retrieval API access to this unparalleled volume of content with a global user base of 15,000,000 researchers, SciVerse Applications offers developers a unique opportunity to create applications which can have a real impact the efficiency of researchers worldwide.
In the past few years, the amount of data published on the Web following the Linked Data principles has increased dramatically. Everyday people are publishing datasets as Linked Data. However, application that make use of Linked Data are not mainstream yet. We invite researchers, developers and practitioner to participate and show the world what can be done with Linked Data, that could not be done before the Linked Data era. The main goal of the Linked Data-a-thon competition is to develop innovative applications that showcase the benefits of Linked Data ... in two weeks! The spirit of this competition is to demonstrate what is possible with Linked Data, even in a short amount of time. The competition starts on October 1, 2011, and it closes 14 days later, on October 15, 2011. During this time each participating team works locally or remotely to develop a Linked Data based application that satisfies the requirements listed below. To enforce the spontaneous, hackathon-like nature of this competition, the applications must satisfy additional requirements that will be announced on October 1, 2011. Anybody can participate in this event and the submitted applications will be presented at the Linked Data gathering during ISWC 2011. Mark these two weeks in your calendar, form a team, and get ready to start your favorite development environment when the Linked Data-a-thon starts on October 1. To express your impatience in the weeks before the start, share the fun and excitement during the Linked Data-a-thon, or to report your progress, we encourage the use of microblogging services or other means of social broadcasting, provided with the hashtag #ldthon2011.
In its third year, the Mentoring Lunch at the International Semantic Web Conference brings together graduate students and early-career researchers with researchers and faculty for a lively discussion and question-answering session on a variety of topics. If you are a PhD student, a postdoc, or have just started an independent research career and would like to get advice on any of the round-table topics listed below, please join us at the specially designated tables during the lunch break on Wednesday, October 26, 2011 in room TBA. The mentors are all volunteers from the speakers, chairs, and other senior participants at the conference. If you would like to get advice from mentors on some of the topics below, please sign up for lunch and answer a few quick questions that would help us with the organization of the lunch. The first 50 people to sign up will be automatically added to the list of attendees, the rest will be put on the waiting list (we are limited by the room capacity and by the number of mentors). While we are organizing only the mentoring lunch, we expect that some of you might want to have a more sustained relationship with the mentors and we invite you to ask the mentors at lunch about such a possibility.
The Minute Madness session will be organized before the poster and demo track and will consist of 1-minute presentations about the exhibits of the track. The event's goal is twofold: on the one hand, it gives an opportunity to poster and demo presenters to advertise their exhibit to a large audience and therefore ensure that all interested participants will visit their booth during the main track. On the other hand, this one hour session will provide conference participants with a quick (and fun!) overview of the presented works so that they can make the best use of their time during the session and visit the exhibits which are most interesting for them.
The common vision of the Semantic Web is the achievement of seamless and transparent interoperability of services across diverse providers by making the meaningful content of those services openly accessible. For content to become �meaningful� it must be related to more general information schemes for which some semantics has already been provided. Such information schemes range from tag collections, which impose an indirect semantics by means of induced similarity across tagged entities to formal ontologies, which represent explicitly class-subclass relationships, the attributes of such classes as well as additional information that may be known about combinations of such classes and their instances. Whereas the semantic information that is explicitly maintained in ontologies might appear ideal for realizing the Semantic Web vision, substantial problems still render this goal difficult in practice. The symposium will address some of these problem areas.
This symposium aims at proliferating the understanding of semantics, especially its usage within enterprises. On the one hand, speakers from industry show applications of semantics within various projects relevant within different application areas ranging from environmental information portals to automation and mechanical engineering. On the other hand, technologies developed within the Core technology Cluster of the THESEUS programme and now ready for usage in different applications are presented and demonstrated. These tools cover technologies like semantic search, reasoning, mapping of ontologies, disambiguation of entities and managing changes within knowledge bases. Within a panel discussion aspects like funding, strategies to bring semantics into real-world applications and future research with respect to future applications of semantics are discussed. Bringing together researchers and representatives from industry is a special focus of this workshop. Its co-location with the ISWC (International Semantic Web Conference) and the invitation of a keynote speaker like Mark Greaves as well as having speakers from industry serves as a basis to foster synergies between both groups, which can then culminate in cooperative European research projects in the field of Future Internet as shown by keynote speaker Bernard Barani.
It has become a tradition at ISWC to come together in a town hall meeting to have conference participants share ideas on what they would like to see at the future ISWCs and to discuss what works and what does not work at the conference. Please join the members of the conference organizing committee in an informal discussion about all the new events that we added to the conference program this year and tell us what you would like to see in the future and what you liked and didn't like this year.
Everything Self‐Service: Linked Data Applications with the Information Workbench
dipLODocus[RDF]--Short and Long-Tail RDF Analytics for Massive WebsofData
FedX: Optimization Techniques for Federated Query Processing on LinkedData
strukt---A Pattern System for Integrating Individual and Organizational Knowledge Work
Link Prediction for Annotation Graphs using Graph Summarization
Effectively Interpreting Keyword Queries on RDF Databases with a RearView
DBpedia SPARQL Benchmark Performance Assessment with Real Queries on Real Data
Extending Logic Programs with Description Logic Expressions for the SemanticWeb
Semantic Information Management: Future funding by EU and beyond
Ten rules to make your semantic app addictive � revisited
Aalto University and University of Helsinki
Aalto University School of Science, Dept. of Media Technology and University of Helsinki, Dept. of Computer Science
Alexandru Ioan Cuza University, Faculty of Computer Science, Iasi
Bone Dysplasia Research Group, UQ Centre for Clinical Research (UQCCR), The University of Queensland
Bonn-Aachen International Center for Information Technology B-IT
CENTRIA, FCT, Universidade Nova de Lisboa
Computer Science Department. University of Valladolid
CSIRO Australian E-Health Research Centre
CyberShARE Center, University of Texas at El Paso
Data Mining Information Retrieval Group, University of Würzburg
University of Vienna, Department of Distributed and Multimedia Systems
Department of Biology, University of North Carolina, Chapel Hill, NC, USA
Deparments of Computer Engineering and Informatics, University of Patras
Department of Computer Engineering and Informatics, University of Patras
Department of Computer Science and Technology, Nanjing University
Department of Computer Science and Technology, University of Peloponnese
Department of Computer Science, Iowa State University, Ames, IA, USA
Department of Computer Science, VU University Amsterdam
Department of Computer Sciences, Institute for Cell and Molecular Biology, The University of Texas at Austin
Depts. of Medicine and Human Genetics, Computation Institute and Institute of Genomics and Systems Biology, University of Chicago
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Digital Enterprise Research Institute (DERI), NUI Galway
Digital Enterprise Research Institute, NUI Galway
DERI - National University of Ireland, Galway
DERI, National University of Ireland, Galway
Digital Enterprise Research Institute, National University of Ireland, Galway
DERI, National University of Ireland galway
Digital Enterprise Research Institute (DERI), Galway
Deutsches Archäologisches Institut - Römisch-Germanische Kommission
Dipartimento di Informatica Sistemistica e Comunicazione, Universitta degli Studi di Milano-Bicocca
Dipartimento Sistemi di Produzione, Consiglio Nazionale delle Ricerche, Italy
ESIS Norge AS / Oslo Municipality - Popsenteret
Faculty of Computer Science, Free University of Bolzano
Faculty of Information Technology - Czech Technical University in Prague
Faculty of Mathematics and Physics, Charles University in Prague
Fraunhofer Institute for Applied Information Technology FIT
Free University of Bolzano-Bozen, Faculty of Computer Science
Freie Universität Berlin, Networked Information Systems
Freie Universität Berlin, Web-based Systems Group
FTW Telecommunications Research Center Vienna
GeoVISTA Center, Department of Geography; Pennsylvania State University, USA
German Research Center for Artificial Intelligence (DFKI)
German Research Center for Artificial Intelligence
GESIS - Leibniz Institute for the Social Sciences
GESIS-Leibniz Institute for the Social Sciences
Helsinki University of Technology (TKK) and University of Helsinki
Hypios & STIH, Université Paris-Sorbonne, France
IBM T.J. Watson Research, Deep QA – Algorithms Team
ICCS, National Technical Unversity of Athens, Greece
Information Technology, the Open University
INSERM UMRS 872, éq.20, 15, rue de l’école de médecine, 75006 Paris, France
AIFB, Karlsruhe Institute of Technology
AIFB, Karlsruhe Institute of Technology, Germany
Institute AIFB, Karlsruhe Institute of Technology
Institute AIFB - Karlsruhe Institute of Technology
School of Computer Science and Engineering, Southeast University
Institute AIFB/KSRI, University of Karlsruhe (TH)
Institut fuer Informatik, University of Freiburg
Institut für Informationssysteme, Technische Universität Wien
Institut National de la Santé et de la Recherche Médicale
Institute for Geoinformatik, University of Muenster
Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology
Institute of Information Systems 184/3, Vienna University of Technology
Institute of Neuroscience, University of Oregon
Institute of Software, the Chinese Academy of Sciences
Intelligent Software Components (iSOCO) S.A.
Istituto per i beni artistici, culturali e naturali della Regione Emilia-Romagna
Karlsruher Institut für Technologie (KIT)
Knowledge Media Institute (KMi), The Open University
Knowledge Media Institute, the Open University
Knowledge Media Institute - The Open University
Knowledge Media Institute, Open University, UK
Knowledge Media Institute, The Open University, UK
Kno.es.is Center, Wright State University, Dayton, Ohio Faculty of Computer Science, Universitas Indonesia
Knowledge Data Engineering Group, University of Kassel
Knowledge Management Institute - Graz University of Technology
Korea Institute of Science and Technology Information
KRDB Research Centre, Free University of Bozen-Bolzano
L3S Research Center Leibniz Universität Hannover
Lab of Medical Physics, Medical School, Democritus University of Thrace
Laboratoire d’Informatique de l’universit Paris-Nord (LIPN) - UMR 7030 Universit Paris 13 - CNRS
Research Institute for Linguistics,Hungarian Academy of Sciences
Research Institute for Linguistics,Hungarian Academy of Sciences,
Lockheed Martin Advanced Technology Laboratories
Massachusetts General Hospital / Harvard Medical School
Max-Planck Institute for Evolutionary Anthropology
National and Kapodistrian University of Athens
The National Institute of Advanced Industrial Science and Technology
Institute of Informatics Telecommunications, NCSR "Demokritos"
University of the Aegean and National Centre of Scientific Research "Demokritos"
Nomadis Research Lab., University of Milano-Bicocca
Ontology Engineering Group. Laboratorio de Inteligencia Artificial. Facultad de Informática. Universidad Politécnica de Madrid.
Ontology Engineering Group. Facultad de Informática. Universidad Politécnica de Madrid
Ontology Engineering Group. Universidad Politécnica de Madrid
Otto-vonOtto-von-Guericke-Universität Magdeburg - Fakultät für Informatik - AG Managementinformationssysteme
Oxford University Computing Laboratory, UK
Pontificia Universidad Católica de Chile
Poznan Supercomputing and Networking Center
PReCISE Research Center, University of Namur
Salzburg Research Forschungsgesellschaft
School of Automation, Northwestern Polytechnical University
School of Computing, University of Leeds
School of Informatics, University of Edinburgh, UK
School of ITEE, The University of Queensland
Semantic Computing Research Group (SeCo), Aalto University School of Science and Technology (TKK)
Semantic Computing Research Group (SeCo), Aalto University School of Science, Department of Media Technology
Servizio sviluppo amministrazione digitale e sistemi informativi geografici, Regione Emilia-Romagna
Department of Computer Science Engineering, Shanghai Jiao Tong University
Department of Computer Science & Engineering, Shanghai Jiao Tong University
Dept. of Computer Science Engineering, Shanghai Jiao Tong University
Stanford Center for Biomedical Informatics Research
Tampere University of Technology, Hypermedia Laboratory
Technical University of Dresden, Dept. of Informatics, BIOTEC
Tetherless World Constellation, Rensselaer Polytechnic Institute
The Genomics Institute of the Novartis Research Foundation
Department of Computer Science, Tsinghua University
Department of Computer Science. University of Chile
Universidad Politécnica de Madrid, Spain
Universidad Simon Bolivar, Dept. Computer Science
University of Leipzig, Department of Computer Science
Universität Leipzig, Institut für Informatik
University of Applied Science Northweastern Switzerland
University of Arkansas for Medical Sciences
Computer Science Department - University of Bari
University of Bremen, SFB/TR 8 Spatial Cognition
University of Helsinki and Aalto University School of Science
University of Koblenz-Landau, Computer Science
University of Maryland, Baltimore County
Institute for Geoinformatics, University of Münster
Insitut for Geoinformatics, University of Münster
Intsitut for Geoinformatics, University of Münster
University of Science & Technology of China
Department of Computer Sciences, The University of Texas at Austin
University of Zurich, Dynamic and Distributed Information Systems Group
Uppsala University, Department of Information Technology
VU University Amsterdam, The Netherlands
University of Amsterdam/Vrije Universiteit Amsterdam
Web Science Program, Mathematics Department, Aristotle University of Thessaloniki, Greece
Kno.e.sis Center, Wright State University
Kno.e.sis Center, Wright State University, Dayton, Ohio
Kno.e.sis Center, Wright State University, Dayton, OH
The quantity of published Linked Data is increasing dramatically. However, applications that consume Linked Data are not yet widespread. Current approaches lack methods for seamless integration of Linked Data from multiple sources, dynamic discovery of available data and data sources, provenance and information quality assessment, application development environments, and appropriate end user interfaces. Addressing these issues requires well-founded research, including the development and investigation of concepts that can be applied in systems which consume Linked Data from the Web. Following the success of the 1st International Workshop on Consuming Linked Data, we organize the second edition of this workshop in order to provide a platform for discussion and work on these open research problems. The main objective is to provide a venue for scientific discourse - including systematic analysis and rigorous evaluation - of concepts, algorithms and approaches for consuming Linked Data.
Second International Workshop on Consuming Linked Data
Workshop Chair at ISWC2011 (COLD Workshop)
Crowdsourcing tasks within Linked Data management
Defining and Executing Assessment Tests on Linked Data for Statistical Analysis
Incremental SPARQL Evaluation for Query Answering on Linked Data
The Information Workbench as a Self-Service Platform for Linked Data Applications
LDIF - Linked Data Integration Framework (Systems Paper)
Consuming Linked Data within a Large Educational Organization
Identifying Information Needs by Modelling Collective Query Patterns
Using Tag Clouds to Quickly Discover Patterns in Linked Data Sets
SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions
A Research Agenda for Linked Closed Data
LODWheel – JavaScript-based Visualization of RDF Data
Extracting core knowledge from Linked Data
In recent years, researchers in several communities involved in aspects of the web have begun to realise the potential benefits of assigning an important role to events in the representation and organisation of knowledge and media. The goal of this workshop is to advance research on this general topic within the semantic web community, focusing on topics such as detection and extraction of events, modeling and representation of events, and the use of events within applications, including visualization and interaction. Prior to the workshop, a dataset containing linked events will be made available and a prize is awarded to those participants that submit the best application/analysis based on this data.
Detection, Representation, and Exploitation of Events in the Semantic Web
EvoDyn builds on the success of the previous editions of the Ontology Dynamics workshop formerly known as IWOD (organised as a part of the ESWC'07, ISWC'08, ISWC'09 and ISWC'10 conferences). EvoDyn continues in the tradition of IWOD in being the core annual event to discuss advances in the broad area of ontology dynamics, and to track recent work directly or indirectly related to the problem of evolving ontologies. This year, however, the scope of the workshop is broadened by a special focus on the knowledge evolution. In particular, the workshop focuses on analysis of trends and change in formal descriptions (i.e., ontologies), but also in associated raw sources of knowledge (scientific publications, unstructured or semi-structured web content, traditional data stores, e-mail or on-line discussion threads, etc.). We are especially interested in research targeted on various states of knowledge evolution, such as (a) conflicts, (b) consolidation, (c) discovery, (d) paradigm shifts, and (e) breakthroughs. One crucial objective of better understanding these different states may be to study directly the underlying causes and dynamics needed to generate discoveries and breakthroughs. We will only be able to facilitate and possibly also generate such desirable situations if we can understand the process of how knowledge evolves. The process of how knowledge in a field grows and changes, crystallizes, and fractures are all areas of interest of this workshop.
Joint Workshop on Knowledge Evolution and Ontology Dynamics
Workshop Chair at ISWC2011 (EvoDyn Workshop)
Statistical Analysis of Web of Data Usage
The Linked Data initiative gained momentum inside as well as outside of theresearch community. Thus, it is already an accepted research issue to investigate usage mining in the context ofthe Web of Data from various perspectives. We are currently working onan approach that applies such usage mining methods and analysis to support ontology and datasetmaintenance tasks. This paper presents one part of this work, namely a methodto detect errors or weaknesses within ontologies used for Linked Data populationbased on statistics and network visualizations. We contribute a detailed description of a log file preprocessing algorithm for Web of Data endpoints, a set of statistical measures that help to visualize different usage aspects, and an examplary analysis of one of the most prominent Linked Data set -- DBpedia -- aimed to show the feasibility and the potential of our approach.
As ontologies evolve, it becomes important to be able to dis-cover how they have changed over time. In the recent past there havebeen a few very useful tools based on the Manchester OWL API thataddress this issue. However, these new tools do not take into accounthow to align entities in two ontologies when the names of the entitieshave changed. In this case, we need to be able to discover the alignmentbetween the entities in the two ontologies before can match up the struc-tures in the two ontologies. In this paper, we describe a highly optimizedpluggable difference engine that searches for alignments between entitiesin different ontology versions and applies those alignments to display thedifferences in the ontologies. We discuss our experiences applying thetools to a selection of ontologies from the BioPortal ontology reposi-tory, including the performance and accuracy of the tool.
Facilitating the Analysis of Ontology Differences
The analysis of changes between OWL ontologies (in the form of a diff) is an important service for ontology engineering. A purely syntactic analysis of changes is insufficient to distinguish between changes that have logical impact and those that do not. The current state of the art in semantic diffing ignores logically ineffectual changes and lacks any further characterisation of even significant changes. We present and demonstrate a diff method based on an exhaustive categorisation of effectual and ineffectual changes between ontologies. In order to verify the applicability of our approach we apply it to 88 versions of the National Cancer Institute (NCI) Thesaurus (NCIt), and 5 versions of SNOMED CT, demonstrating that all categories are realized throughout the corpus. Based on the outcome of these studies we argue that the devised categorisation of changes is helpful for ontology engineers and their understanding of changes carried out between ontologies.
Learning Vague Concepts for the Semantic Web
Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.
Towards automatic language evolution tracking, A study on word sense tracking
Knowing about language evolution can significantly help to reveal lost information and help access documents containing language that has long since been forgotten. In this position paper we will report on our methods for finding word senses and show how these can be used to reveal important information about their evolution over time. We discuss the weaknesses of current approaches and outline future work to overcome these weaknesses.
RETRO: A Framework for Semantics Preserving SQL-to-SPARQL Translation
There have been several attempts to make RDBMS and RDFstores inter-operate. The most popular one, D2RQ, has explored onedirection i.e. to look at RDBMS through RDF lenses. In this paper wepresent RETRO, which investigates the reverse direction i.e. to look atRDF through Relational lenses. RETRO generates a relational schemafrom an RDF store, enabling a user to query RDF data using SQL.A significant advantage of this direction in-addition to interoperabilityis that it makes numerous relational tools developed over past severaldecades, available to the RDF stores. In order to provide interoperabilitybetween these two DB systems one needs to resolve the heterogeneitybetween their respective data models and include schema mapping, datamapping and query mapping in the transformation process [1]. However,like D2RQ, RETRO chooses not to physically transform the data anddeals only with schema mapping and query mapping. RETRO’s schemamapping derives a domain specific relational schema from RDF dataand its query mapping transforms an SQL query over the schema into aprovably equivalent SPARQL query, which in-turn is executed upon theRDF store. Since RETRO is a read-only framework, its query mappinguses only a relevant and relationally complete subset of SQL. A proofof correctness of this transformation is given based on compositionalsemantics of the two query languages.
Graph-based Discovery of Ontology Change Patterns
Ontologies can support a variety of purposes, ranging fromcapturing conceptual knowledge to the organisation of digital contentand information. However, information systems are always subject tochange and ontology change management can pose challenges. We investigateontology change representation and discovery of change patterns.Ontology changes are formalised as graph-based change logs. We useattributed graphs, which are typed over a generic graph with node andedge attribution.We analyse ontology change logs, represented as graphs,and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usage driven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental results.
Balancing brave and cautious query strategies in ontology debugging
Sequential ontology debugging aims to the efficient discrimination between diagnoses. By querying additional information the debugger can gradually reduce the number of diagnoses to be considered by the user. The selection of the best queries is of central importance for minimizing diagnosis costs. If prior fault probabilities are available, the best results are achieved by entropy based selection methods.However, given some weakly justified priors these methods bravely suggest suboptimal queries. In such a case, it is more efficient to use a no-risk method which prefers queries that eliminate 50% of diagnoses independently of any fault probabilities. However, choosing the appropriate method in advance is impossible because the quality of given priors cannot be assessed before additional information is queried.In this paper we propose a method which combines advantages of both approaches. On the one hand the method takes into account available fault probabilities and the user
Scientific efforts are traditionally published only as articles, with an estimate of millions of publications worldwide per year; the growth rate of PubMed alone is now 1 papers per minute. The validation of scientific results requires reproducible methods, which can only be achieved if the same data, processes, and algorithms as those used in the original experiments were available. However, the problem is that although publications, methods and datasets are very related, they are not always openly accessible and interlinked. Even where data is discoverable, accessible and assessable, significant challenges remain in the reuse of the data, in particular facilitating the necessary correlation, integration and synthesis of data across levels of theory, techniques and disciplines. In the 1st International Workshop on Linked Science (LISC 2011) we will discuss and present results of new ways of publishing, sharing, linking, and analyzing such scientific resources motivated by driving scientific requirements, as well as reasoning over the data to discover interesting new links and scientific insights.
1st International Workshop on Linked Science
Workshop Chair at ISWC2011 (LISC Workshop)
Linking the Outcomes of Scientific Research: Requirements from the Perspective of Geosciences
Supporting Scientific Collaboration Through Class-Based Object Versioning
Similarity between semantic description sets: addressing needs beyond data integration
Interactively Mapping Data Sources into the Semantic Web
Functional Requirements for Bibliographic Resources
Where did you hear that? Information and the Sources They Come From
The knowledge-driven exploration of integrated biomedical knowledge sources facilitates the generation of new hypotheses
Glottolog/Langdoc: Defining dialects, languages, and language families as collections of resources
Multilingualism has become an issue of major interest for the Semantic Web community, in light of the substantial growth of internet users that create and update knowledge all over the world in languages other than English. This process has been accelerated due to initiatives such as the Linked Data initiative, which encourages not only governments and public institutes to make their data available to the public, but also private organizations in domains as far apart as medicine, cartography or music. These actors publish their data sources in the languages they are available in, and as such, in order to make this information available to an international community, multilingual knowledge representation, access and translation are an impending need. Building on the success of the first edition of this workshop, we have organized a second workshop focusing on topics such as the representation of multilingual information in the Semantic Web, cross-lingual querying, multilingual mapping, machine translation, and localization strategies for the Semantic Web.
2nd Workshop on the Multilingual Semantic Web
ULiS: An Expert System on Linguistics to Support Multilingual Management of Interlingual Semantic Web Knowledge bases
We are interested in bridging the world of natural language and theworld of the semantic web in particular to support multilingual access to theweb of data. In this paper we introduce the ULiS project, that aims at designinga pivot-based NLP technique called Universal Linguistic System, 100% usingthe semantic web formalisms, and being compliant with the Meaning-Texttheory. Through the ULiS, a user could interact with an interlingual knowledgebase (IKB) in controlled natural language. Linguistic resources themselves arepart of a specific IKB: The Universal Lexical Knowledge base (ULK), so thatactors may enhance their controlled natural language, through requests in controllednatural language. We describe a basic interaction scenario at the systemlevel, and provide an overview of the architecture of ULiS. We then introducethe core of the ULiS: the interlingual lexical ontology (ILexicOn), in whicheach interlingual lexical unit class (ILUc) supports the projection of its semanticdecomposition on itself. We validate our model with a standalone ILexicOn,and introduce and explain a concise human-readable notation for it.
OntoVerbal-M is an ontology verbaliser that transforms OWL intofluent natural language paragraphs in multiple languages. We describe theapplication of OntoVerbal-M to SNOMED CT, whereby SNOMED CT classesare presented as textual paragraphs in both English and Mandarin through theuse of natural language generation. SNOMED CT is a large description logicbased terminology for recording in electronic health records. Often, neither thelabels nor the description logic definitions in SNOMED CT are easy for usersto understand. Furthermore, information is increasingly being recorded, not justusing individual SNOMED CT concepts, but using dynamically createddescription logic expressions (“post-coordinated” concepts). Such postcoordinatedexpressions can have no pre-assigned labels. In this contextautomatic verbalisation into multiple languages will be useful both forunderstanding and quality assurance of SNOMED CT definitions, and forhelping different language-speaking-users to understand and share postcoordinatedexpressions.
Accessing Multilingual Data on the Web for the Semantic Annotation of Cultural Heritage Texts
Our study targets interoperable semantic annotation of CulturalHeritage or eHumanities texts in German and Hungarian. A semantic resourcewe focus on is the Thompson Motif-index of folk-literature (TMI), the labels ofwhich are available only in English. We investigate the use lexical data on theWeb in German and Hungarian for supporting semi-automatic translation ofTMI: lexical resources offered by Wiktionary accessed via the Lexvo service,and discuss shortcomings of those resources. An approach for mapping theXML dump of Wiktionary onto a TEI and MAF compliant data is presented,whereby we discuss improvements in the representation of Wiktionary data forexploiting its multilingual value within the LOD framework.
Representing Translations on the Semantic Web
The increase of ontologies and data sets published in theWeb in languages other than English raises some issues related to therepresentation of linguistic (multilingual) information in ontologies. Suchlinguistic descriptions can contribute to the establishment of links betweenontologies and data sets described in multiple natural languagesin the Linked Open Data cloud. For these reasons, several models havebeen proposed recently to enable richer linguistic descriptions in ontologies.Among them, we nd lemon, an RDF ontology-lexicon model thatdenes specic modules for dierent types of linguistic descriptions. Inthis contribution we propose a new module to represent translation relationsbetween lexicons in dierent natural languages associated to thesame ontology or belonging to dierent ontologies. This module can enablethe representation of dierent types of translation relations, as wellas translation metadata such as provenance or the reliability score oftranslations.
Linking Domain-Specific Knowledge to Encyclopedic Knowledge: an Initial Approach to Linked Data
Linked Data creates a shared information space by publishing andconnecting resources in the Semantic Web. However, the specification ofsemantic relationships between data sources is still a stumbling block. Onesolution is to enrich ontologies with multilingual and concept-orientedinformation. Usefully linking entities in the Semantic Web is thus facilitated bya semantic-oriented cross-lingual ontology mapping framework in whichknowledge representations are not restricted to a particular natural language.Accordingly, this paper describes a preliminary approach for integrating generalencyclopedic knowledge in DBpedia with EcoLexicon, a multilingualterminological knowledge base on the environment.
This paper focuses on the conversion between the open standard XML Localisation Interchange File Format (XLIFF) and the Resource Description Framework (RDF). XLIFF is a localisation standard supported by proprietary and free and open source software (FOSS) localisation tools, while the latter is a standard model, basic ingredient in Semantic Web. We developed a converter based on Saxon XSLT Processor which translates XLIFF to RDF.
Direct and Indirect Linking of Lexical Objects for Evolving Lexical Linked Data
Servicization of language resources in a Web-based environ-ment has opened up the potential for dynamically combined virtual lex-ical resources. Evolving lexical linked data could be realized, providedbeing recovered/discovered links among lexical resources are properlyorganized and maintained. This position paper examines a scenario, inwhich lexical semantic resources are cross-linguistically enriched, andsketches how this scenario could come about while discussing necessaryingredients. The discussions naturally include how the existing lexiconmodeling framework could be applied and should be extended.
Cross-Lingual Web API Classification and Annotation
Recent developments on the Web are marked by the growingsupport for the Linked Data initiative, which encourages governmentand public organisations, as well as private institutions, to expose theirdata on the Web. This results in a plentitude of multi-lingual documentcollections where the original resources are published in the language,in which they are available. The challenges of multilingualism presenton the Semantic Web are also reected in the context of services on theWeb, characterised by the rapid increase in popularity and use of WebAPIs, as indicated by the growing number of available APIs and theapplications built on top of them. Web APIs are commonly described inplain-text as part of Web pages, following no particular guidelines andconforming to no standards, despite some initial approaches in the area[1, 2]. Therefore, API providers publish descriptions in any language theysee t, making the service discovery and the subsequent processing of thedocumentation challenging tasks. In this paper, we present a cross-lingualapproach that calculates semantic similarity of text to help classify andannotateWeb APIs, based on their textual descriptions. Furthermore, weshow how our solution can be implemented as part of SWEET [3], whichis a tool that enables the semi-automated creation of semantic Web APIdescriptions. In addition, we demonstrate how the cross-lingual approachcan be adopted to support the language-independent discovery of WebAPIs.
Language Technology and Interoperability
A Semantic Model for Integrated Content Management, Localisation and Language Technology Processing
Providers of products and services are faced with the dual challengeof supporting the languages and individual needs of the global customer whilealso accommodating the increasing relevance of user-generated content. As aresult, the content and localisation industries must now evolve rapidly frommanually processing predicable content which arrives in large jobs to the highlyautomated processing of streams of fast moving, heterogeneous andunpredictable content. This requires a new generation of digital contentmanagement technologies that combine the agile flow of content fromdevelopers to localisers and consumers with the data-driven languagetechnologies needed to handle the volume of content required to feed thedemands of global markets. Data-driven technologies such as statisticalmachine translation, cross-lingual information retrieval, sentiment analysis andautomatic speech recognition, all rely on high quality training content, which inturn must be continually harvested based on the human quality judgments madeacross the end-to-end content processing flow. This paper presents themotivation, approach and initial semantic models of a collection of researchdemonstrators where they represent a part of, or a step towards, documenting ina semantic model the multi-lingual semantic web.
OntoVerbal-M: a Multilingual Verbaliser for SNOMED CT
The real challenge for Semantic Web technologies and ontologies lays in the adoption; although the need for this disruptive technology is clear, it has not yet been fully adopted by the mainstream. Ontologies: where, what for, how, when and why? Ontologies are being used in several applications, but is ontology engineering a mature discipline? Not only are we interested in practical realizations of the Semantic Web, but also in visions of technology that illustrate how SW technology and ontologies could change our experience of the Web.
Ontologies come of Age in the Semantic Web
Workshop Chair at ISWC2011 (OCAS Workshop)
Generic Multilevel Approach Designing Domain Ontologies based on XML Schemas
Designing an ontology for a specific domain is a time-consuming process. In many cases information sources like XML Schemas serve as a basis for ontology engineers to conceptualize the intended ontologies. The ontology design process is sped up significantly when XML Schemas are transformed automatically into generated ontologies. An XML Schema Metamodel Ontology has been designed to represent the components of the XML Schema abstract data model. The generated ontologies’ classes are defined as sub classes of this ontology. The classes specified for the generated ontologies are intended to be further supplemented with additional semantic and domain specific information defined in domain ontologies. The resulting ontologies are as usable as ontologies that were constructed completely manual, but with a fraction of necessary effort.
Extending Ontologies with Free Keywords in a Collaborative Annotation Environment
Semantic web technologies have introduced the idea of annotating content in terms of concepts taken from ontologies. Since concepts are defined in terms of properties and relations to other concepts, descriptions grow up into larger RDF graphs that can be used as a basis for data integration and intelligent information retrieval. Since ontologies are typically incomplete, it is usually necessary to offer the annotator the possibility to introduce new free keywords or tags in addition to the predefined ontology concepts. The problem then is that free keywords/tags do not have ontological connections to the rest of the RDF graph, unless such relations are defined by the annotator. We present a process for integrating free keywords into the ontological framework, and a practical tool implementation of it, discussing the challenges and possibilities introduced by the system. We also describe a case study performed for the Finnish Defence Forces, where the tool is used for creating a faceted semantic search portal featuring the free keywords and the ontological concepts at the same time.
MUTU: An Analysis Tool for Maintaining a System of Hierarchically Linked Ontologies
We consider ontology evolution in a system of light-weight LinkedData ontologies, aligned with each other to form a large ontology system. Whenchanges occur in one of the ontologies, it is difficult for the human editor to keeptrack of what actually has been changed and what modifications may be needed inthe related ontologies in order to keep the system consistent. This paper presentsan analysis toolMUTU, by which such changes and their potential effects to otherontologies can be found. Such an analysis is useful for the ontology editors forundestanding the differences between ontology versions, and for updating relatedontologies when changes occur in other components of the ontology system.
In this position paper we analyze the similarities amongst folksonomies, semantic wikis, and ontology building; we also propose an alignment and orchestration of ontologies representing these scenarios. We argue that such alignment enables a more direct application of folksonomy-based approaches over these set-ups. The rationale behind folksonomies is shared across environments such as collaborative ontology building and semantic wikis. The three of them aim to facilitate knowledge sharing across communities; a social environment in which individual and community objectives are achieved supports them all.
Dynamic is-a Hierarchy Generation for User Centric Semantic Web
In ontological theory, is-a hierarchy must represent essential property of things and hence it should be single-inheritance, since essential property of things cannot be multiple. However, we cannot avoid multi-perspective issues when we build an ontology because the user often want to understand things from their own viewpoints. Especially, in the Semantic Web, the variety of user causes the variety of viewpoint to capture target domains. In order to tackle this multi perspective issue, we adopt an approach of dynamically generating is-a hierarchies according to the viewpoint of users from an ontology using single-inheritance. This article discusses a framework for dynamic is-a hierarchy generation and its implementation as extended function of Hozo. Through the function, the users can understand an ontology from their own viewpoints.
Ontology-Based Features Recognition and Design Rules Checker System
We designed and implemented an Ontology-Based System for Features Recognition and Design Rules Checking. It was developed using Semantic Web technologies. It was implemented by means of the Protégé Application Programming Interface (API), a rule engine and a reasoner. Using ontology in the core of the system enabled the representation of manufacturing rules and Automatic Features recognition. Rules and Queries were not hard coded in the program, so we gave to the system a high level of maintainability and reusability. Features were classified as general and specifics, easing the work of classifying newer features as they appear, given their previous classification. Most common sheet metal features referred in the literature were recognized by the system.
iCAT: A Collaborative Authoring Tool for ICD-11
We present iCAT—the Collaborative Authoring Tool for the 11th revision of the International Classification of Diseases (ICD-11). ICD is a fundamental health-care resource developed by the World Health Organization (WHO) and applied in all United Nations countries for a variety of uses. A landmark change in this ICD-11 revision compared to previous ones is the use of OWL as a representation language and of Semantic Web technologies for the collaborative authoring of the ICD content. A community of international medical experts develops ICD-11 in a collaborative setting using the Web-based iCAT platform. Besides its extensive collaboration support, iCAT also fosters the interconnectedness of ICD-11 with other biomedical ontologies and terminologies by providing interlinking and reusing capabilities as a fundamental feature of the tool, while also storing the provenance metadata. The generic and extensible infrastructure as well as its declarative user interface allowed us to easily deploy iCAT in a production setting for the development of ICD-11 and two other WHO classifications. The declarative user interface allowed us to custom tailor the platform for domain experts use. iCAT is in production use since October 2009; it was used to author over 105,000 changes in the ICD-11 ontology, to create more than 40,000 cross-links to other biomedical ontologies, and to produce over 19,000 notes and discussions. The software is open source and a demo version of the platform is available at http://icatdemo.stanford.edu.
Ontologies Come of Age with the iKUP Browser
The iKUP browser, a web-based application to the kidney and urinary pathway knowledge base, shows ontologies coming of age by enabling biologists to ask questions of integrated data that form hypotheses that are being tested in the laboratory. That is, semantically annotated data, that are integrated through rich interconnections, have been delivered to the target users in a form that they can use to change how they undertake their job.
Socio-technical Ontology Development for Modelling Sensemaking in Heterogeneous Domains
Sensemaking is often associated with processing large or complex amount of data obtained from diverse and distributed sources. With information explosion from the web, sensemaking is becoming ubiquitous and ever more challenging. Semantic technologies have potential to support understanding of sensemaking process with the benefits they bring (e.g. reasoning, aggregation, automation). Conceptual models of sensemaking have been developed to understand its complex processes by social and information scientist; however, these frameworks are not applicable directly to system design. This paper describes a socio-technical approach for modelling sensemaking process in order to inform the development of intelligent services to aid sensemakers. We apply an a priori ontology modularisation methodology for handling complexity of heterogeneous domains and utilise well-known sensemaking theoretical framework to guide ontology development. This approach is applied in an EU project - Dicode, for the development of its sensemaking ontology.
Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful tactic in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes the ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data translation, query answering or navigation on the web of data. Thus, matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate. The workshop has three goals: To bring together leaders from , and institutions to assess how academic advances are addressing real-world requirements. The workshop will strive to improve academic awareness of industrial and final user needs, and therefore direct research towards those needs. Simultaneously, the workshop will serve to inform industry and user representatives about existing research efforts that may meet their requirements. The workshop will also investigate how the ontology matching technology is going to evolve. To conduct an extensive and rigorous evaluation of ontology matching approaches through the OAEI (Ontology Alignment Evaluation Initiative) 2011 campaign. The particular focus of this year's OAEI campaign is on real-world specific matching tasks involving, e.g., open linked data and biomedical ontologies. Therefore, the ontology matching evaluation initiative itself will provide a solid ground for discussion of how well the current approaches are meeting business needs. To examine similarities and differences from database schema matching, which has received decades of attention but is just beginning to transition to mainstream tools.
The Sixth International Workshop on Ontology Matching
The goal of the ORDRING 2011 workshop is to bring together the growing and very active community of Semantic Web researchers and practitioners interested in novel ideas, experiments, and application visions originating from the efforts aimed at interleaving ordering and reasoning.
First International Workshop on Ordering and Reasoning
Reasoning over Dynamic Data in Expressive Knowledge Bases with Rscale
We introduce Rscale, a secondary storage-aware OWL 2 RL reasoning system capable of dealing with incremental additions and deletions of facts. Our initial evaluation indicates that Rscale is suitable for stream reasoning scenarios characterized by expressive reasoning tasks triggered by a moderate change frequency.
BetterRelations: Detailed Evaluation of a Game to Rate Linked Data Triples
While associations between concepts in our memory have different strengths, the explicit strengths Linked Data edge weights are missing. As a result Linked Data is of limited use for Artificial Intelligence with respect to the simulation of human thinking. In order to build a collection of such edge weights, we created a web-game prototype that ranks triples by importance. In this paper we explain the game's concept, summarize Linked Data related implementation aspects, and include a detailed evaluation of the game.
Representation and Conformance of UML models containing ordered properties using OWL2
In this article we show how to represent UML models depicting ordered properties using OWL2, and how to reason about model conformance using OWL2 reasoners. Our translation from UML models to OWL2 is driven by three important forces. First, we want to maintain the close-world assumption about UML models. Second, we want to preserve structural model information of ordered properties. Finally, model conformance is defined solely by OWL2 axioms so that reasoning can be done by using existing and future OWL2 reasoner developed by others. We have implemented the translation as an automatic model transformation tool. The model transformation tool takes as input a UML object model and its class model and produces an ontology that can be processed by an OWL2 reasoner to reveal if the object model elements conform to their class model or not.
Towards Efficient Schema-Enhanced Pattern Matching over RDF Data Streams
Data streams, often seen as sources of events, have appeared on the Web. Event processing on the Web needs however to cope with the typical openness and heterogeneity of the Web environment. Semantic Web technology, meant to facilitate data integration in an open environment, can help to address heterogeneities across multiple streams. In this paper we discuss an approach towards efficient pattern matching over RDF data streams based on the Rete algorithm, which can be considered as a first building block for event processing on the Web. Our approach focuses on enhancing Rete with knowledge from the RDF schema associated with data streams, so that implicit knowledge can contribute to pattern matching. Moreover, we cover Rete extensions that cope with the streaming nature of the processed data, such as support for temporal operators, time windows, consumption strategies and garbage collection.
High-performance Distributed Stream Reasoning using S4
In this paper, we present a high-throughput system for reasoning over RDF streams. Recognising the inherent limitations of sequential approaches, we turn to parallel methods, utilizing a compute cluster and the Yahoo S4 framework. We are presenting a set of low-level predicates operating on a stream and an encoding of RDFS reasoning and C-SPARQL query answering using these predicates.We perform experiments on up to 32 compute nodes. Our results indicate that our method scales well and can process hundreds of thousands of triples per second.
Data on the Semantic Web is ordered, for instance, through orderingsover literals (e.g., age, heights, location, rating etc), resources(e.g., popularity) or triples (e.g., provenance, confidence, time-stamps).Recently, orderings have been established as first class citizensin an extended SPARQL algebra, with an efficient calculus for finding, e.g.,top-k answers. In this paper we advocate a similar initiative for thesemantic (or reasoning) part of such querying: given an expressiveknowledge base with orderings, can we make use of these orderings toefficiently reason over large datasets where top-k answers are required.The basic idea is to define the notion of a top-k closure, which isa subset of the full semantic closure sufficient to answertop-k queries. We argue that this top-k closure can be efficiently calculated by applying top-k joins needed when applying entailment rules of the standard calculus.This paper introduces the top-k reasoning problem, and advocates a potential solution.
PC Member at ISWC2011 (OrdRing Workshop)
The 4th international workshop on Social Data on the Web (SDoW2011) aims to bring together researchers, developers and practitioners involved in semantically-enhancing social media websites, as well as academics researching more formal aspect of these interactions between the Semantic Web and Social Web, including people involved in recent active developments in collaborative and social software and their Semantic Web counterparts, notably in the industry, such as Facebook Open Graph Protocol. It is now widely agreed in the community that the Semantic Web and the Social Web can benefit from each other. One the one hand, the speed at which data is being created on the Social Web is growing at exponential rate. Recent statistics showed that about 100 million Tweets are created per day and that Facebook has now 500 million users. Yet, some issues still have to be tackled, such as how to efficiently make sense of all this data, how to ensure trust and privacy on the Social Web, how to interlink data from different systems, whether it is on the Web or in the enterprise, or more recently, how to link Social Network and sensor networks to enable Semantic Citizen Sensing. Following the successful SDoW workshops at ISWC 2008, 2009 and 2010, this workshop will tackle these various topics and aims at bringing together researchers and practitioners, as in the 3 previous editions.
4th International Workshop on Social Data on the Web
Workshop Chair at ISWC2011 (SDOW Workshop)
LexiTags: An Interlingua for the Social Semantic Web
The paper describes lexitags, a new approach to social semantic tagging whose goal is to allow users to easily enrich resources with semantic metadata from WordNet. This is a paradigm example of the Social Web and the Semantic Web working together: ordinary users help create the metadata so needed by the Semantic Web and in turn, Semantic Web technologies help those users get a richer experience from the Social Web. A family of simple user interfaces for lexitagging is described, as are some methods for the subsequent, automatic generation of lightweight ontologies. These ontologies are presented as an ideal interlingua for the Social Semantic Web.
Volatile Classification of Point of Interests based on Social Activity Streams
Location sharing services(LSS) like Foursquare, Gowalla and Facebook Places gather information from millions of users who leave trails in locations (i.e. “chekins”) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collective perception of a POI can be used as a real-time dataset from which POI
Semantic Technologies to Support the User-Centric Analysis of Activity Data
There is currently a trend in giving access to users of online services to their own data. In this paper we consider in particular the data which is generated from the interactions between a user and an organisation online: activity data as held in website and web application logs. We show how, we use semantic technologies including RDF integration of log data, SPARQL and lightweight ontology reasoning to aggregate, integrate and analyse activity data from a user-centric point of view.
Social Network Aggregation Using Face-Recognition
With the rapid growth of the social web an increasing num- ber of people started to replicate their off-line preferences and lives in an on-line environment. Consequently, the social web provides an enormous source for social network data, which can be used in both commercial and research applications. However, people often take part in multiple social network sites and, generally, they share only a selected amount of data to the audience of a specific platform. Consequently, the interlinkage of social graphs from different sources getting increasingly important for applications such as social network analysis, personalization, or recommender systems. This paper proposes a novel method to enhance available user re-identification systems for social network data aggregation based on face-recognition algorithms. Furthermore, the method is combined with traditional text-based approaches in order to attempt a counter-balancing of the weaknesses of both methods. Using two samples of real-world social networks (with 1610 and 1690 identities each) we show that even though a pure face-recognition based method gets out- performed by the traditional text-based method (area under the ROC curve 0.986 vs. 0.938) the combined method significantly outperforms both of these (0.998, p = 0.0001) suggesting that the face-based method indeed carries complimentary information to raw text attributes.
Pragmatic metadata matters: How data about the usage of data effects semantic user models
Online social media such as wikis, blogs or message boardsenable large groups of users to generate and socialize around content.With increasing adoption of such media, the number of users who interactwith user-generated content and perform certain activities on it (suchas authoring content, replying to it or liking it) grows. As a result,the amount of pragmatic metadata - i.e. data about the usage of data- is growing as well. This paper sets out to explore if and how suchmetadata has an eect on the quality of semantic user models that canbe learned from user generated content. To this end, we use data from anonline message board (the Irish boards.ie), incorporate dierent types ofpragmatic metadata into topic modeling algorithms and evaluate themby comparing their predictive performance. Our evaluation is based onthe assumption that "better" user models will be able to predict futurecontent of users more accurately and will need less time and trainingdata. Our results suggest that not all types of pragmatic metadata areequally useful for acquiring accurate semantic user models, and sometypes of pragmatic metadata can even have detrimental eects. One ofour key observations is that semantic models of users which are based onthe content to which they replied (rather than the content which theyauthored) perform better than others. To the best of our knowledge, thisis the rst work that demonstrates an eect between pragmatic metadataon one hand, and the quality of semantic user models based on user-generated content on the other. Our results are relevant for scientistsinterested in topic modeling algorithms and pragmatic metadata, andfor engineers interested in semantic analysis of users and textual contentin social media.
Towards Mining Semantic Maturity in Social Bookmarking Systems
The existence of emergent semantics within social metadata(such as tags in bookmarking systems) has been proven by a large numberof successful approaches making the implicit semantic structures explicit.However, much less attention has been given to the factors which influencethe ’maturing’ process of these structures over time. A natural hypothesisis that tags become semantically more and more mature whenever manyusers use them in the same contexts. This would allow to describe atag by a specific and informative ’semantic fingerprint’ in the context oftagged resoures. However, the question of assessing the quality of suchfingerprints has been seldomly addressed.In this paper, we provide a systematic approach of mining semantic ma-turity profiles within folksonomy-based tag properties. Our ultimate goalis to provide a characterization of ’mature tags’. Additionally, we con-sider semantic information about the tags as a gold-standard source forthe characterization of the collected results. Our initial results suggestthat a suitable composition of tag properties allows the identification ofmore mature tag subsets. The presented work has implications for a number of problems related to social tagging systems, including tag ranking,tag recommendation, and the capturing of light-weight ontologies fromtagging data.
Linked Opinions: Describing Sentiments on the Structured Web of Data
In the paper we report on the results of our experiments on the construction of the opinion ontology. Our aim is to show the benefits of publishing in the open, on the Web, the results of the opinion mining process in a structured form. On the road to achieving this, we attempt to answer the research question to what extent opinion information can be formalised in a unified way. Furthermore, as part of the evaluation, we experiment with the usage of Semantic Web technologies and show particular use cases that support our claims.
Folksonomies can be viewed as large sources of informal semantics. Folksonomy tagscan be interpreted as concepts that can be extracted from the social data and usedas a basis for creating semantic structures. In the folksonomy the connection betweenthese concepts and the tagged resources are explicit. However, to effectively use theextracted conceptual structures it is important to be able to find connections betweenthe concepts and not only the already tagged documents, but also new documents that havenot previously been seen. Thus, we present in this paper an automatic approach forannotating documents with concepts extracted from social data. This is based on representingeach tag
Using SKOS to Integrate Social Networking Sites with Scholarly Information Portals
Web 2.0 platforms have become a ubiquitous way of information exchange, but are seldom integrated with the Web of Data. To overcome this situation we propose the usage of SKOS thesauri acting as back-of-the-book index and providing domain-specific axes transcending application domains. We illustrate this concept with a use-case in the social sciences domain, but applications in other domains are possible.
In the Social Web, the users are invited to publish a lot of personal information. These information can be easily retrieved, and sometimes reused, without providing the users with fine-grained access control mechanisms able to restrict the access to their profiles, and resources. In this paper, we present an access control model for the Social Semantic Web. Our model is grounded on the Social Semantic SPARQL Security for Access Control Ontology. This ontology can be used by the users to define, thanks to an Access Control Manager, their own terms of access to the data. Moreover, the Access Control Manager allows to check, after a query, to which extent the data is available, depending on the user’s profile. The evaluation of the access conditions is related to different features, such as social tags, contextual information, being part of a group, and relationships with the data provider.
Search engines implementing the canonical search paradigm are adequate for most ad-hoc keyword-based search tasks, but they reach limits when user needs have to be satisfied in a personalized way. With the advent of the Semantic Web, new opportunities emerge for semantic information retrieval systems to better match user needs. Next generation search engines should implement a novel search paradigm, where the user perspective is completely reversed: from finding to being found. Recommender Systems may help to support this new perspective, because they have the effect of pushing relevant objects to potentially interested users. An emerging approach is to use Semantic Web technologies to model information about users, their needs and preferences, their context and relations, and to incorporate data from other resources like Linked Open Data. The aim of the workshop is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can significantly improve the search/recommendation process in those tasks that cannot be solved merely through a straightforward matching of queries and documents.
2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation
The 4th Semantic Sensor Networks workshop aims to provide an inter-disciplinary forum to explore and promote the technologies related to a combination of semantic web and sensor networking. Specifically, to develop an understanding of the ways semantic web technologies can contribute to the growth, application and deployment of large-scale sensor networks on the one hand, and the ways that sensor networks can contribute to the emerging semantic web, on the other.
The 4th International Workshop on Semantic Sensor Networks
SSWS 2011 is the seventh edition of the successful Scalable Semantic Web Knowledge Base Systems workshop series. This workshop provides a forum for discussing scalability issues for the Semantic Web, with the focus on the development and deployment of knowledge base systems for processing Semantic Web data. We expect that scalability issues are going to challenge the Semantic Web for a long time and significant effort is needed in order to tackle them. This workshop brings together researchers and practitioners to share their recent ideas and advances towards building scalable knowledge base systems for the Semantic Web.
The 7th International Workshop on Scalable Semantic Web Knowledge Base Systems
Workshop Chair at ISWC2011 (SSWS Workshop)
The Quad Economy of a Semantic Web Ontology Repository
Publishers of Linked Data require scalable storage and retrieval in- frastructure due to the size of datasets and potentially high rate of lookups on popular sites. In this paper we investigate the feasibility of using a distributed key-value store as an underlying storage component for a Linked Data server which provides functionality for serving Linked Data via HTTP lookups and in addition offers single triple pattern lookups. We devise two storage schemes for our CumulusRDF system implemented on Apache Cassandra, an open-source key-value store. We compare the schemes with a state-of-the-art distributed RDF store on a subset of DBpedia and both synthetic workloads and workloads ob- tained from DBpedia’s access logs. Results on a cluster of up to 8 machines show that CumulusRDF is competetive to state-of-the-art distributed RDF stores.
Conjunctive query answering is a key reasoning service for many ontology-basedapplications. With the advent of lightweight ontology languages, such as OWL2 QL, several query answering systems have been proposed which compute the socalled emph{UCQ rewriting} of a given query. It is often the case in realisticscenarios, that users refine their original queries, by e.g., extending themwith new constraints and making them more precise. To the best of ourknowledge, in such cases, all OWL 2 QL systems would need to recompute therewriting of the refined query from scratch. In this paper we study the problemof computing the rewriting of the refined query by `extending
Since the first publication of RDF researchers developed systems to store and to query RDF data efficiently. First, they mapped the RDF data model to the relational one. As relational databases performed poorly for generic RDF graphs researches focused on native repositories using B-trees to index triples in most cases. However, native repositories still calculate the result of a query by joining single triples.Because queries can be decomposed into star-like graph patterns we argue that managing RDF data as a set of subgraphs has advantages over existing approaches. Thus, we propose a storage model that manages an RDF graph as a collection of subgraphs and evaluates queries by joining the results of star-like subqueries. In cause of the results of some preliminary tests we are confident that a system based on our approach can efficiently process queries.
Sound and Complete SHI Instance Retrieval for 1 Billion ABox Assertions
One of the more mysterious aspects of RDF (Resource Description Framework) is typed literals. For instance, confusion over the difference between a plain character string ("foo") and a string that is explicitly typed ("foo"^^xsd:string) is common. Also, questions often arise about comparisons between literals of the various numeric types (e.g., long, integer, decimal, and float). This paper explores how several popular triple stores handle literals via direct testing, and also compares their behavior to the relevant standards. Along the way, we highlight a number of implementation inconsistencies and some surprising aspects of the standards themselves.
CumulusRDF: Linked Data Management on Nested Key-Value Stores
SAQ (Semantic Archive and Query) is a system for long-term preservation and querying of relational data in terms of RDF. In SAQ relational data in a back-end DBMS is exposed as an RDF view, called the RD-view. SAQ can process arbitrary SPARQL queries to the RD-view. Long-term preservation as RDF of selected parts of a relational database is specified by SPARQL queries to the RD-view. Such queries usually select sets of RDF properties and thus in the query definition a property p is unknown. We call such queries unbound-property queries. This class of queries is also present in the SPARQL benchmarks. We optimize unbound-property queries by introducing a query transformation algorithm called Group Common Terms, GCT. It pulls out from a DNF normalized query those common terms that can be translated to SQL predicates accessing the relational database. Our experiments using the Berlin SPARQL benchmark show that GCT improves substantially the query execution time to a back-end commercial relational DBMS for both selective and unselective unbound-property queries. We compared the performance of our approach with the performance of other systems processing SPARQL queries over views of relational databases and showed that GCT improves scalability compared to the approaches used by the other systems.
Probabilistic Abox Abduction in Description Logics
In recent years, there has been an explosion of publicly availableSemantic Web data. In order to effectively integrate millions ofsmall, distributed data sources and quickly answer queries, we previouslyproposed a tree structure query optimization algorithm that takes thesource selectivity of each query subgoal as the heuristic to plan the queryexecution and uses the most selective subgoals to provide constraintsthat make other subgoals selective. However, this constraint propagationis incomplete when the relevant ontologies contain cyclic axioms. Here,we propose an improvement to this algorithm that is complete for cyclicaxioms, yet still able to scale to millions of data sources.
Optimizing Unbound-property Queries to RDF Views of Relational Databases
Quad stores have a number of features that make them very attractive for Semantic Web applications. Quad stores use Named Graphs to give contextual information to RDF graphs. Developers can use these contexts to incorporate meta-data such as provenance and versioning. The OWL language specification provides the owl:imports construct as one of the key ways to reuse the ontologies on the Semantic Web. When one ontology imports another through the owl:imports statement, all axioms from the imported ontology are brought to the source ontology. When implementing an online ontology repository, we have explored a number of different approaches to reflect these imports and contextual information in a quad store. These approaches have a direct impact on storage requirements, query articulation, and reusability.This paper describes different models to represent contextual information, ontology imports and versioning. We have performed the experiments in the context of storing ontologies from the BioPortal ontology repository. This repository has 304 ontologies, and multiple versions for many of them. We extract metrics on the levels of imports, and the storage and performance requirements and discuss the trade-offs between query articulation, reusability and scalability.
Query Rewriting Under Query Extensions for OWL 2 QL Ontologies
ABox abduction is the process of finding statements that should be added to an ontology to entail a specific conclusion. In this paper, we propose an approach for probabilistic abductive reasoning for SHIQ. Our evaluations show that the proposed approach significantly extends classical abduction by effectively and correctly estimating probabilities for abductive explanations. Lastly, based on the ideas proposed for SHIQ, we describe a tractable algorithm for probabilistic abduction in DL-lite_R.
Ontology Based Data Integration Over Document and Column Family Oriented NoSQL stores
In the last years, reasoning over very large ontologies became more and more important. In our contribution, we propose a reasoning infrastructure, which can perform instance checking and instance retrieval over ontologies with semi-expressive terminological knowledge and large assertional parts.The key idea is to 1) use modularizations of the assertional part, 2) use some kind of intermediate structure to find similarities between individual modules, and 3) store information efficiently on external memory. For evaluation purposes, experiments on benchmark and real world ontologies were carried out. We show that our reasoning infrastructure can handle up to 1 billion ABox assertions. To the best of our knowledge this is the first system to provide sound and complete reasoning over SHI ontologies with such a huge number of assertions.
Handling Cyclic Axioms in Dynamic, Web-Scale Knowledge Bases
The World Wide Web infrastructure together with its more than 2 billion users enables to store information at a rate that has never been achieved before.This is mainly due to the will of storing almost all end-user interactions performed on some web applications.In order to reply to scalability and availability constraints, many web companies involved in this process recently started to design their own data management systems.Many of them are referred to as NoSQL databases, standing for
Monday, October 24th 09:00 12:30 Room: Rheinaue
7th International Workshop on Semantic Web Enabled Software Engineering
The wide availability of technologies such as GPS, map services and social networks, has resulted in the proliferation of geospatial data on the Web. Researchers have been quick to realize the importance of this development and have started working on the relevant research problems, giving rise to new topical research areas such as Geographic Information Retrieval, Geospatial (Semantic) Web, Linked Geospatial Data and GeoWeb 2.0. Similarly, standardization bodies such as the Open Geospatial Consortium (OGC) have been developing relevant standards such as the Geography Markup Language (GML) and GeoSPARQL. The goal of this workshop is to bring together researchers and practitioners from related disciplines, as well as interested parties from industry and government, to advance the frontiers of this exciting research area.
The goal of this workshop is to bring together researchers and practitioners from related disciplines, as well as interested parties from industry and government, to advance the frontiers of this exciting research area.
7th International Workshop on Uncertainty Reasoning for the Semantic Web
There has been more and more evidence that the usageof Semantic Web technologies leads to improvements in both the process and product of software development activities. The goal of the SWESE workshop is to advance research on this this important area. We believe that the informal nature of the workshop, located at one of the major events of Semantic Web, will lead to further exchange between practitioners and researchers working on issues related to Semantic Web Enabled Software Engineering by providing a forum for discussing the major challenges of the area and the different approaches being taken to resolve them
Workshop on Web Scale Knowledge Extraction