logics for data and knowledge representation web ontology language (owl) feroz farazi

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Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

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Page 1: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Logics for Data and Knowledge Representation

Web Ontology Language (OWL)

Feroz Farazi

Page 2: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL

Web Ontology Language designed to be used when the document

content is necessary to be processed by applications instead of

making it understandable only by humans [OWL Overview]

It can be used to represent ontology

Vocabulary terms and the relationships between them

Concepts and relations between them

Provides more facilities than RDF and RDF Schema

In the representation of semantics

In performing reasoning tasks

Page 3: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL Sublanguages

There are three sublanguages of OWL OWL Lite: trades expressivity for efficiency OWL DL: a balance between expressivity and computational

completeness OWL Full: trades computational completeness for expressivity

OWL Lite supports Encoding simple classification hierarchy (e.g., taxonomy and thesaurus) Assigning cardinality constraints 0 or 1

OWL DL supports More expressive than OWL Lite while guarantees conclusions and decidability Using all OWL constructs, some of them with certain restrictions The restriction of not making a class an instance of another class

Page 4: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL Sublanguages

OWL DL is named so because of its connection with description

logics, which form the formal basis of OWL

OWL Full an extension of RDF with maximum expressiveness, e.g., a class can be

represented also as an individual

For these sublanguages the following statements can be made:

Each OWL Lite representation belongs to OWL DL

Each OWL DL representation belongs to OWL Full

Each valid OWL Lite conclusion is also valid in OWL DL

Each valid OWL DL conclusion is also valid in OWL Full

Page 5: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL Lite In OWL Lite

users are allowed to use a subset of the OWL, RDF and RDFS vocabulary

to define a class, one must use the OWL construct owl:Class

OWL constructs, namely: complementOf, disjointWith, hasValue, oneOf

and unionOf are not allowed

Some OWL Constructs are allowed to use but their use is limited

all three cardinality constructs – cardinality, maxCardinality and

minCardinality, can only have 0 or 1 in their value fields

Moreover, equivalentClass and intersectionOf cannot be used in a triple if

the subject or object represents an anonymous class

Page 6: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL DL

In OWL DL Each individual must be an extension of a class Even if an individual cannot be classified under any user defined

class, it must be classified under the general owl:Thing class Individuals can not be used as properties, and vice versa Moreover, properties can not be used as classes, and vice versa It is allowed to use the intersectionOf construct with any

number of classes and of any non negative integer in the cardinality restrictions value fields

The computational complexity is the same as the corresponding Description Logic

Page 7: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Properties Inverse

Given that a property P is inverse of another property Q,P owl:inverseOf Q, and two individuals x and y are connected using P as follows: x P y. We can infer that y Q x.

For example, the property hasChild can be an inverse property of hasParent

Symmetric Given that a property P is symmetric,

P rdf:type owl:symmetricProperty, two individuals x and y are connected using P as follows: x P y. We can infer that y P x.

For example, the property isMarriedTo is symmetricTransitive property is used with owl:TransitiveProperty

Page 8: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Properties Equivalent Property

In RDFS, x rdfs:subPropertyOf y y rdfs:subPropertyOf x

In OWL, x owl:equivalentProperty y For example, buy and purchase can be equivalent properties

Functional Property A functional property can have only one value attached to it for any

individual Given that a property P is functional,

P rdf:type owl:FunctionalProperty, the individuals x, y and z are connected using P as follows: x P y and x P z. We can infer that y owl:sameAs z.

For example, the property hasMother is functional

Page 9: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Properties Inverse Functional Property

An inverse functional property can have only one individual as a subject attached to it for any value

Given that a property P is inverse functional, P rdf:type owl:InverseFunctionalProperty, the individuals x, y

and z are connected using P as follows: x P y and z P y. We can infer that x owl:sameAs z.

For example, the property motherOf is inverse functional Used

Especially in settings where data come from multiple sources

In entity matching on the Semantic Web

Page 10: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

• OWL 2:– Extends OWL 1– Inherits OWL 1 language features

• The new features of OWL 2 based on:– Real applications– User experience– Tool developer experience

OWL 2

Page 11: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Syntactic sugar• New constructs for properties• Extended datatypes• Punning• Extended annotations• Some innovations• Minor features

Page 12: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Syntactic sugar– Makes some patterns easier to write– Does not change• Expressiveness• Semantics• Complexity

– Can help implementations• For more efficient processing

Page 13: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Syntactic sugar:– DisjointUnion– DisjointClasses– NegativeObjectPropertyAssertion– NegativeDataPropertyAssertion

• DisjointUnion• Union of a set of classes• All the classes are pairwise disjoint

Page 14: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Syntactic sugar• Need for disjointUnion construct

– A :CarDoor is exclusively either • a :FrontDoor, • a :RearDoor or • a:TrunkDoor • and not more than one of them

• A disjointUnion example– <owl:Class rdf:about="CarDoor">

<owl:disjointUnionOf rdf:parseType="Collection"> <rdf:Description rdf:about="FrontDoor"/> <rdf:Description rdf:about="RearDoor"/>

<rdf:Description rdf:about="TrunkDoor"/> </owl:disjointUnionOf>

</owl:Class>

Page 15: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Syntactic sugar

• DisjointClasses– A set of classes– All the classes are pairwise disjoint

• Need for DisjointClasses– Nothing can be both• A LeftLung and• A RightLung

Page 16: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Syntactic sugar

• NegativeObjectPropertyAssertion– Two individuals– A property does not hold between themExample, Patient “John” does not live in “Povo”

• NegativeDataPropertyAssertion– An individual– A literal– A property does not hold between themExample, “John” is not “5” years old.

Page 17: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

New constructs for properties

• Self restriction• Qualified cardinality restriction• Object properties• Disjoint properties• Property chain• keys

Page 18: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Self restriction

• Classes of objects that are related to themselves by a given property

• For example, the class of processes that regulate themselves

• It is also called local reflexivity• An example: Auto-regulating processes

regulate themselves

Page 19: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Qualified cardinality restrictions

• Qualifies the instances to be counted• Restrain the class or data range of the

instances to be counted• For example, – Persons that have exactly three children who are

girls– Each individual has at most one SSN

Page 20: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Qualified cardinality restrictions

• ObjectMinCardinality• ObjectMaxCardinality• ObjectExactCardinality• DataMinCardinality• DataMaxCardinality• DataExactCardinality

Page 21: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Object properties

• ReflexiveObjectProperty– Globally reflexive– Everything is part of itself

• IrreflexiveObjectProperty– Nothing can be a proper part of itself

• AsymmetricObjectProperty– If x is proper part of y, then the opposite does not hold

Page 22: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Disjoint propertis

• DisjointObjectProperties– Deals with object properties– Pairwise disjointness can be asserted– E.g., connectedTo and contiguousWith

• DisjointDataProperties– Deals with data properties– Pairwise disjointness can be asserted– E.g., startTime and endTime of a surgery

Page 23: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Property chain inclusion

• Properties can be defined as a composition of other properties

• If disease A is locatedIn body part B and B is part of body part C then A is locatedIn C

• SubObjectPropertyOf ( ObjectPropertyChain( locatedIn partOf) locatedIn)

Page 24: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Keys

• Individuals can be identified uniquely• Identification can be done using– A data property– An object property or– A set of properties

• HasKey( :RegisteredPatient :hasWaitingListN ) ClassAssertion( :RegisteredPatient :ThisPatient ) DataPropertyAssertion( :hasWaitingListN :ThisPatient "123-45-6789" )

• HasKey( :Transplantation :donorId :recipientId :ofOrgan )

Page 25: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Syntactic sugar• New constructs for properties• Extended datatypes• Punning• Extended annotations• Some innovations• Minor features

Page 26: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Extended datatypes

• Extra datatypes– For example, owl:real and owl:rational

• Datatype restrictions– Range of datatypes– For example, adult has an age >= 18– DatatypeRestriction(xsd:integer minInclusive 18)

• Datatype definitions– New datatypes– DatatypeDefinition( :adultAge DatatypeRestriction(xsd:integer

minInclusive 18))

Page 27: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Extended datatypes

• Data range combinations– Intersection of• DataIntersectionOf( xsd:nonNegativeInteger

xsd:nonPositiveInteger )

– Union of• DataUnionOf( xsd:string xsd:integer )

– Complement of data range• DataComplementOf( xsd:positiveInteger )

Page 28: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Punning

• Punning: “What's black and white and red all over?”

• Classes and individuals can have the same name thanks to punning– E.g., Eagle as a class and as an individual

• Properties and individuals can have the same name– E.g., is_located_in as a property and as an

individual of Deprecated_Properties class

Page 29: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Punning

• Classes and object properties also can have the same name

• But classes and datatype properties can not have the same name

• Also datatype properties and object properties can not have the same name

Page 30: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Extended Annotations– Axioms can be annotated– For example, SubClassOf( Annotation( rdfs:comment "Middle lobes of

lungs are necessarily right lobes since left lungs do not have middle lobe.") :MiddleLobe :RightLobe )

• Innovations– Top and Bottom properties– IRI: Internationalized Resource Identifier

Page 31: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Features and Rationale

• Inverse object properties:– some object property can be inverse of another

property– For example, partOf and hasPart– ObjectInverseOf( :partOf ): this expression

represents the inverse property of :part of– This makes writing ontologies easier by avoiding

the need to name an inverse

Page 32: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Profiles

• Profiles are sublanguages of OWL 2• Profiles considered– Useful computational properties, e.g., reasoning

complexity– Implementation possibilities, e.g., using RDBs

• There are three profiles– OWL 2 EL– OWL 2 QL– OWL 2 RL

Page 33: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL 2 EL• The EL acronym reflects the profile’s basis in the

EL family of description logics• This logic is also called small description logic (DL)

EL• This logic allows for conjunction and existential

restrictions• It does not allow disjunction and universal

restrictions• It can capture the expressive power used by

many large-scale ontologies, e.g., SNOMED CT

Page 34: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL 2 QL• The QL acronym reflects its relation to the

standard relational Query Language• It does not allow existential and universal

restrictions to a class expression or a data range• These restrictions – enable a tight integration with RDBMSs, – reasoners can be implemented on top of standard

relational databases• Can answer complex queries (in particular,

unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base

Page 35: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

OWL 2 RL• The RL acronym reflects its relation to the Rule

Languages• OWL 2 RL is desgined to accommodate– OWL 2 applications that can trade the full expressivity of

the language for efficiency– RDF(S) applications that need some added expressivity

from OWL 2• Existential quantification to a class, union and disjoint

union to class expressions are not allowed• These restrictions – allow OWL 2 RL to be implemented using rule-based

technologies such as rule extended DBMSs

Page 36: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

Profiles

• Profile selection depends on– Expressivenss required by the application– Priority given to reasoning on classes or data– Size of the datasets

Page 37: Logics for Data and Knowledge Representation Web Ontology Language (OWL) Feroz Farazi

References OWL Overview (2004). W3C Recommendation. OWL 2 New Features and Rationale (2009). W3C Recommendation. F. Giunchiglia, F. Farazi, L. Tanca, and R. D. Virgilio. The semantic web

languages. In Semantic Web Information management, a model based perspective. Roberto de Virgilio, Fausto Giunchiglia, Letizia Tanca (Eds.), Springer, 2009.

D. Allemang and J. Hendler. Semantic web for the working ontologist: modeling in RDF, RDFS and OWL. Morgan Kaufmann Elsevier, Amsterdam, NL, 2008.