This is the RDF Schema for the RDF vocabulary defined in the RDF namespace.
The subject of the subject RDF statement.
The predicate of the subject RDF statement.
The empty list, with no items in it. If the rest of a list is nil then the list has no more items in it.
The object of the subject RDF statement.
The rest of the subject RDF list after the first item.
Idiomatic property used for structured values.
The class of containers of alternatives.
The Friend of a Friend (FOAF) RDF vocabulary, described using W3C RDF Schema and the Web Ontology Language.
An image that can be used to represent some thing (ie. those depictions which are particularly representative of something, eg. one's photo on a homepage).
A person known by this person (indicating some level of reciprocated interaction between the parties).
A .plan comment, in the tradition of finger and '.plan' files.
A workplace homepage of some person; the homepage of an organization they work for.
A document that this thing is the primary topic of.
Indicates the name (identifier) associated with this online account.
A textual geekcode for this person, see http://www.geekcode.com/geek.html
The birthday of this Agent, represented in mm-dd string form, eg. '12-31'.
A foaf:LabelProperty is any RDF property with texual values that serve as labels.
A link to the publications of this person.
A tipjar document for this agent, describing means for payment and reward.
A short informal nickname characterising an agent (includes login identifiers, IRC and other chat nicknames).
A location that something is based near, for some broadly human notion of near.
An organization funding a project or person.
A weblog of some thing (whether person, group, company etc.).
Indicates the class of individuals that are a member of a Group
A homepage of a school attended by the person.
A work info homepage of some person; a page about their work for some organization.
Indicates an account held by this agent.
A Myers Briggs (MBTI) personality classification.
The gender of this Agent (typically but not necessarily 'male' or 'female').
A page about a topic of interest to this person.
An agent (eg. person, group, software or physical artifact).
A project this person has previously worked on.
The primary topic of some page or document.
A personal mailbox, ie. an Internet mailbox associated with exactly one owner, the first owner of this mailbox. This is a 'static inverse functional property', in that there is (across time and change) at most one individual that ever has any particular value for foaf:mbox.
A checksum for the DNA of some thing. Joke.
Indicates a homepage of the service provide for this online account.
A string expressing what the user is happy for the general public (normally) to know about their current activity.
A thing depicted in this representation.
The sha1sum of the URI of an Internet mailbox associated with exactly one owner, the first owner of the mailbox.
The underlying or 'focal' entity associated with some SKOS-described concept.
A project (a collective endeavour of some kind).
A phone, specified using fully qualified tel: URI scheme (refs: http://www.w3.org/Addressing/schemes.html#tel).
The class of plain (i.e. untyped) literal values.
RENCI UNC-Chapel Hill 2009-2011 yxin@renci.org, ibaldin@renci.org
Allows specify what specific CE this is e.g. in a request - a ComputeElement <ceInstance specificCE EucaC1Medium>
CoberturaDeTomate
Note that hasTopping is inverse functional because isToppingOf is functional
CoberturaDeMozzarella
CoberturaDeCalabreza
CoberturaDePimentaoVerdePicante
CoberturaDeJalapeno
NaoPicante
A property created to be used with the ValuePartition - Spiciness.
CoberturaPeperonata
MolhoTobascoPepper
CoberturaDeCamarao
CoberturaDeCebola
Picante
CoberturaDeCaper
CoberturaDeAnchovies
CoberturaDePresunto
CoberturaDeAzeitona
CoberturaDeQueijoDeCabra
CoberturaDeTomateRessecadoAoSol
Pizza
CoberturaDeQueijo
CoberturaDeEspinafre
CoberturaDeParmesao
CoberturaDeAlho
CoberturaDeCogumelo
CoberturaDeFrutosDoMarMistos
Media
CoberturaDeTomateFatiado
CoberturaDeLeek
CoberturaPetitPois
CoberturaDeFrutas
CoberturaDeCarne
Any pizza that does not have fish topping and does not have meat topping is a VegetarianPizza. Members of this class do not need to have any toppings at all.
PizzaVegetariana
CoberturaDeAspargos
BaseDaPizza
CoberturaDeCajun
CoberturaDeCebolaVermelha
CoberturaDePimentaoDoce
CoberturaDeFrango
CoberturaRosemary
CoberturaQuatroQueijos
BaseFinaEQuebradica
CoberturaDeGorgonzola
CoberturaDeArtichoke
CoberturaDePimentaoVerde
CoberturaDeBifePicante
CoberturaRocket
Any pizza topping that has spiciness Hot
CoberturaTemperada
CoberturaDaPizza
CoberturaDePeixe
An example of a covering axiom. VegetarianTopping is equivalent to the union of all toppings in the given axiom. VegetarianToppings can only be Cheese or Vegetable or....etc.
CoberturaVegetariana
CoberturaEmMolho
CoberturaDeVegetais
CoberturaDeErvas
CoberturaDeCastanha
CoberturaPineKernels
CoberturaSultana
NB Transitive - the ingredients of ingredients are ingredients of the whole
Any given instance of topping should only be added to a single pizza (no cheap half-measures on our pizzas)
A class that is equivalent to the set of individuals that are described in the enumeration - ie Countries can only be either America, England, France, Germany or Italy and nothing else. Note that these individuals have been asserted to be allDifferent from each other.
Pais
CoberturaDePimentao
A ValuePartition that describes only values from Hot, Medium or Mild. NB Subclasses can themselves be divided up into further partitions.
Tempero
Food
Hrana
A ValuePartition is a pattern that describes a restricted set of classes from which a property can be associated. The parent class is used in restrictions, and the covering axiom means that only members of the subclasses may be used as values. The possible subclasses cannot be extended without updating the ValuePartition class.
ValorDaParticao
BaseEspessa
The inverse property tree to hasIngredient - all subproperties and attributes of the properties should reflect those under hasIngredient.
An example ontology that contains all constructs required for the various versions of the Pizza Tutorial run by Manchester University (see http://www.co-ode.org/resources/tutorials/)
v.1.5. Removed protege.owl import and references. Made ontology URI date-independent
v.1.4. Added Food class (used in domain/range of hasIngredient), Added several hasCountryOfOrigin restrictions on pizzas, Made hasTopping invers functional
Americana
A pizza that can be found on a pizza menu
PizzaComUmNome
QuatroQueijos
AmericanaPicante
Cajun
Cogumelo
SloppyGiuseppe
Veneziana
PolloAdAstra
Caprina
Giardiniera
An unclosed Pizza cannot be inferred to be either a VegetarianPizza or a NonVegetarianPizza, because it might have other toppings.
PizzaAberta
Siciliana
Margherita
Rosa
Parmense
CoberturaPrinceCarlo
Napoletana
Soho
Capricciosa
Fiorentina
FrutosDoMar
LaReine
Any pizza that has at least 1 cheese topping.
PizzaComQueijo
This class will be inconsistent. This is because we have given it 2 disjoint parents, which means it could never have any members (as nothing can simultaneously be a CheeseTopping and a VegetableTopping). NB Called ProbeInconsistentTopping in the ProtegeOWL Tutorial.
CoberturaDeQueijoComVegetais
A class to demonstrate mistakes made with setting a property domain. The property hasTopping has a domain of Pizza. This means that the reasoner can infer that all individuals using the hasTopping property must be of type Pizza. Because of the restriction on this class, all members of IceCream must use the hasTopping property, and therefore must also be members of Pizza. However, Pizza and IceCream are disjoint, so this causes an inconsistency. If they were not disjoint, IceCream would be inferred to be a subclass of Pizza.
Sorvete
Any pizza that has at least 3 toppings. Note that this is a cardinality constraint on the hasTopping property and NOT a qualified cardinality constraint (QCR). A QCR would specify from which class the members in this relationship must be. eg has at least 3 toppings from PizzaTopping. This is currently not supported in OWL.
PizzaInteressante
Any pizza that has at least one meat topping
PizzaDeCarne
Any Pizza that is not a VegetarianPizza
PizzaNaoVegetariana
CoberturaDePrezuntoParma
This defined class has conditions that are part of the definition: ie any Pizza that has the country of origin, Italy is a RealItalianPizza. It also has conditions that merely describe the members - that all RealItalianPizzas must only have ThinAndCrispy bases. In essence, all pizzas from Italy must have ThinAndCrispy bases.
PizzaItalianaReal
Any pizza that has a spicy topping is a SpicyPizza
PizzaTemperada
An alternative definition for the SpicyPizza which does away with needing a definition of SpicyTopping and uses a slightly more complicated restriction: Pizzas that have at least one topping that is both a PizzaTopping and has spiciness hot are members of this class.
PizzaTemperadaEquivalente
Any pizza that only has vegetarian toppings or no toppings is a VegetarianPizzaEquiv1. Should be inferred to be equivalent to VegetarianPizzaEquiv2. Not equivalent to VegetarianPizza because PizzaTopping is not covering
PizzaVegetarianaEquivalente1
An alternative to VegetarianPizzaEquiv1 that does not require a definition of VegetarianTopping. Perhaps more difficult to maintain. Not equivalent to VegetarianPizza
PizzaVegetarianaEquivalente2
PU = U^exponent, where PU power unit is derived from U unit.
A metric prefix or SI prefix is a unit prefix that precedes a basic unit of measure to indicate a decadic multiple or fraction or or binary multiple of the unit. Each prefix has a unique symbol that is prepended to the unit symbol. The use of multiple prefixes are disallowed.
LTU = scale * U + offset, where LTU stands for the linear transformed unit derived from U.
Quantitative attributes are all measurable on interval scales, as any difference between the levels of an attribute can be multiplied by any real number to exceed or equal another difference. A highly familiar example of interval scale measurement is temperature with the Celsius scale. In this particular scale, the unit of measurement is 1/100 of the temperature difference between the freezing and boiling points of water under a pressure of 1 atmosphere. The "zero point" on an interval scale is arbitrary; and negative values can be used. The formal mathematical term is an affine space (in this case an affine line). Variables measured at the interval level are called "interval variables" or sometimes "scaled variables" as they have units of measurement. Ratios between numbers on the scale are not meaningful, so operations such as multiplication and division cannot be carried out directly. But ratios of differences can be expressed; for example, one difference can be twice another. The central tendency of a variable measured at the interval level can be represented by its mode, its median, or its arithmetic mean. Statistical dispersion can be measured in most of the usual ways, which just involved differences or averaging, such as range, interquartile range, and standard deviation. Since one cannot divide, one cannot define measures that require a ratio, such as studentized range or coefficient of variation. More subtly, while one can define moments about the origin, only central moments are useful, since the choice of origin is arbitrary and not meaningful. One can define standardized moments, since ratios of differences are meaningful, but one cannot define coefficient of variation, since the mean is a moment about the origin, unlike the standard deviation, which is (the square root of) a central moment.
Most measurement in the physical sciences and engineering is done on ratio scales. Mass, length, time, plane angle, energy and electric charge are examples of physical measures that are ratio scales. The scale type takes its name from the fact that measurement is the estimation of the ratio between a magnitude of a continuous quantity and a unit magnitude of the same kind (Michell, 1997, 1999). Informally, the distinguishing feature of a ratio scale is the possession of a zero value. For example, the Kelvin temperature scale has a non-arbitrary zero point of absolute zero, which is denoted 0K and is equal to -273.15 degrees Celsius. This zero point is accuracy representing the particles that compose matter at this temperature having zero kinetic energy. All statistical measures can be used for a variable measured at the ratio level, as all necessary mathematical operations are defined. The central tendency of a variable measured at the ratio level can be represented by, in addition to its mode, its median, or its arithmetic mean, also its geometric mean or harmonic mean. In addition to the measures of statistical dispersion defined for interval variables, such as range and standard deviation, for ratio variables one can also define measures that require a ratio, such as studentized range or coefficient of variation.
Base units are the simplest unit forms, independent of each other, and they are the profound building blocks for derived units.
N-ary units are derived from more than one unit.
Unary unit is a derivative of a single unit.
This kind of unit is a made-up unary type. The idea behind is that most of the complex functions can be represented as a series of operations based on regular expressions. RTU = Fforward(U) U = Finverse(RTU)
Numbers are meaningless for the physicist without the correct use of units. It makes no sense to say "the distance from my house to school is two", unless we follow that statement with "miles" or "kilometers", or whichever unit makes the statement true. We will distinguish between dimension, the abstract quality of a measurement without scale (ie., length), and unit, the quality of a number which specifies a previously agreed upon scale (ie., meters). While dimensional arguments are of primary importance in understanding the qualitative behavior of systems, the use of units are necessary for the predictions we seek.
Rank-ordering data simply puts the data on an ordinal scale. Ordinal measurements describe order, but not relative size or degree of difference between the items measured. In this scale type, the numbers assigned to objects or events represent the rank order (1st, 2nd, 3rd, etc.) of the entities assessed. An example of an ordinal scale is the result of a horse race, which says only which horses arrived first, second, or third but include no information about race times. When using an ordinal scale, the central tendency of a group of items can be described by using the group's mode (or most common item) or its median (the middle-ranked item), but the mean (or average) cannot be defined. Thus it is an error to write that an IQ of 160 is just as different from an IQ of 130 as an IQ of 100 is different from an IQ of 70.
ProdU = U1 * U2 [... * Ui], where ProdU product unit is derived from multiplying units Ui.
An abstraction of any arbitrary function that can be expressed by regular expressions. The format can be of the following two forms: I. /evalInteger/expresion/operation/ II. /evalIntegerAsString/expression/operation/separator/[...], where [...] stands for a series of operation/separator/ with n repetition. "expression": is a regular expression, which contains grouping eclosed by parenthesis, like "([0-9]*)". These groups are referenced by "\g<?>" within the operation field, where question mark represents the sequence number of the group referenced, starting with 1. "operation": is a mathematical form that may contain operations understood by eval method. For instance: "+, -, *, /, >>", etc. "separator": is the piece of string to be appended to the result of an evaluation, which is already transformed a string. An example: Given a function that maps: {apple1 |-> 10, apple2 |-> 20, ...} can be expressed as follows: /evalInteger/alma([0-9]*)/10*(\g<1>)/ Another example: Given a function that maps: { 11 |-> 1d2u, 32 |-> 3d4u, ...} can be expressed as follows: /evalIntegerAsString/([0-9])([0-9])/(\g<1>)/d/2*(\g<2>)/u/
At the nominal scale, i.e., for a nominal category, one uses labels; for example, rocks can be generally categorized as igneous, sedimentary and metamorphic. For this scale, some valid operations are equivalence and set membership. Nominal measures offer names or labels for certain characteristics. Variables assessed on a nominal scale are called categorical variables. We can use a simple example of a nominal category: the destination port of an IP packet captured. For instance, looking at the destination port values we might find one or more of IP packets with a value 7777. In this scenario 7777 is their label. We can only check whether two packets' destination ports have the same label (equivalence) or whether a given label is in on a certain list of labels (set membership), but it is impossible to say which label is greater or less than another (comparison) or to measure the difference between two labels. Given a set of destination port numbers, we can describe the set by its most common label (the mode), but cannot provide an "average label" or even the "middle label" among all the names. However, if we decide to sort our labels numerically (or to sort them by how many times they appear in a measurement log), we will begin to turn this nominal scale into an ordinal scale.
Pasulj
likeToEat
Calories
Pizza corner store
A prefix that precedes a basic unit of measure to indicate a decadic multiple or fraction of the unit. The prefix kilo-, for example, may be added to gram to indicate multiplication by one thousand; one kilogram is equal to one thousand grams (1 km = 1000 m). The prefix centi-, likewise, may be added to metre to indicate division by one hundred; one centimetre is equal to one hundredth of a metre (1 cm = 0.01 m).
The "levels of measurement" or scales of measure are expressions that typically refer to the theory of scale types developed by the psychologist Stanley Smith Stevens. Stevens proposed his theory in a 1946 Science article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval" and "ratio".
In computing, a binary prefix is a specifier or mnemonic that is prepended to the units of digital information, the bit and the byte, to indicate multiplication by a power of 2. In practice the powers used are multiples of 10, so the prefixes denote powers of 1024 = 2^10. Starting in about 1998, a number of standards and trade organizations approved standards and recommendations for a new set of binary prefixes, proposed earlier by the International Electrotechnical Commission (IEC), that would refer unambiguously to powers of 1024. According to these, the SI prefixes would only be used in the decimal sense, even when referring to data storage capacities: kilobyte and megabyte would denote one thousand bytes and one million bytes respectively (consistent with SI), while new terms such as kibibyte, mebibyte and gibibyte, abbreviated KiB, MiB, and GiB, would denote 1024 bytes, 1048576 bytes, and 1073741824 bytes respectively.