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Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
Ontologies and Vocabularies
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Ontologies and Vocabularies

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Talk at SSSW2012, Cercedilla. http://www.sssw.org/2012

Talk at SSSW2012, Cercedilla. http://www.sssw.org/2012

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  • 1. Ontologies and Vocabularies Sean Bechhofer School of Computer Science, University of Manchester, UK http://www.cs.manchester.ac.uk Ontology Languages, SSSW12
  • 2. Stuff... 2
  • 3. Stuff...? 3
  • 4. Semantic Web • Metadata – Describe resources – No good unless everyone speaks the same language; • Terminologies – provide shared and common vocabularies of a domain, so search engines, agents, authors and users can communicate. – No good unless everyone means the same thing; • Ontologies – provide a shared and common understanding of a domain that can be communicated across people and applications, and will play a major role in supporting 4
  • 5. Classes/Properties/Individuals • Many representation languages use an “object oriented model” with • Objects/Instances/Individuals – Elements of the domain of discourse • Types/Classes/Concepts – Sets of objects sharing certain characteristics • Relations/Properties/Roles – Sets of pairs (tuples) of objects • Such languages are/can be: – Well understood – Formally specified – (Relatively) easy to use – Amenable to machine processing 5
  • 6. Structure of an Ontology Ontologies typically have two distinct components: • Names for important concepts in the domain – Elephant is a concept whose members are a kind of animal – Herbivore is a concept whose members are exactly those animals who eat only plants or parts of plants – AdultElephant is a concept whose members are exactly those elephants whose age is greater than 20 years • Background knowledge/constraints on the domain – An AdultElephant weighs at least 2,000 kg – All Elephants are either AfricanElephants or IndianElephants 6
  • 7. Why Semantics? • What does an expression in an ontology mean? • The semantics of a language can tell us precisely how to interpret a complex expression. • Well defined semantics are vital if we are to support machine interpretability – They remove ambiguities in the interpretation of the descriptions. Telephone Black ? 7
  • 8. What does RDF give us? • A mechanism for publishing data. • Single (simple) data model. • Syntactic consistency between names (IRIs). • Low level integration of data. – Mash the graphs together and we’re done. 8
  • 9. RDF(S): RDF Schema • RDF gives a data model and serialisations, but it does not give any special meaning to schema vocabulary such as subClassOf or type – Interpretation is an arbitrary binary relation • RDF Schema extends RDF with a schema vocabulary that allows us to define basic vocabulary terms and the relations between those terms – Class, Property – type, subClassOf – range, domain 9
  • 10. RDF(S) • These terms are the RDF Schema building blocks (constructors) used to create vocabularies: – <Person,type,Class> – <hasColleague,type,Property> – <Professor,subClassOf,Person> – <Carole,type,Professor> – <hasColleague,range,Person> – <hasColleague,domain,Person> • Semantics gives “extra meaning” to particular RDF predicates and resources – specifies how terms should be interpreted – allows us to draw inferences 10
  • 11. RDF(S) Inference rdfs:Class rdf:type Person rdf:type rdfs:subClassOf rdf:type Academic rdfs:subClassOf rdf:subClassOf Lecturer 11
  • 12. RDF(S) Inference rdfs:Class rdf:type Academic rdf:type rdfs:subClassOf Lecturer rdfs:type rdf:type Sean 12
  • 13. RDF/RDF(S) “Liberality” • No distinction between classes and instances (individuals) <Species,type,Class> <Lion,type,Species> <Leo,type,Lion> • No distinction between language constructors and ontology vocabulary, so constructors can be applied to themselves/each other <type,range,Class> <Property,type,Class> <type,subPropertyOf,subClassOf> • In order to cope with this, RDF(S) has a particular non- standard model theory. 13
  • 14. What does RDF(S) give us? • Ability to use simple schema/vocabularies when describing our resources. • Consistent vocabulary use and sharing. • Basic inference 14
  • 15. Problems with RDF(S) • RDF(S) is too weak to describe resources in sufficient detail – No localised range and domain constraints  Can’t say that the range of hasChild is Person when applied to Persons and Elephant when applied to Elephants – No existence/cardinality constraints  Can’t say that all instances of Person have a mother that is also a Person, or that Persons have exactly 2 parents – No transitive, inverse or symmetrical properties  Can’t say that isPartOf is a transitive property, that hasPart is the inverse of isPartOf or that touches is symmetrical 15
  • 16. OWL • OWL: Web Ontology Language • Extends existing Web standards – Such as XML, RDF, RDFS • Is (hopefully) easy to understand and use – Based on familiar KR idioms • Of “adequate” expressive power • Formally specified – Possible to provide automated reasoning support 16
  • 17. The OWL Family Tree DAML RDF/RDF(S) DAML-ONT Joint EU/US Committee DAML+OIL OWL OWL2 Frames OIL W3C OntoKnowledge+Others Description Logics 17
  • 18. Aside: Description Logics • A family of logic based Knowledge Representation formalisms – Descendants of semantic networks and KL-ONE – Describe domain in terms of concepts (classes), roles (relationships) and individuals • Distinguished by: – Formal semantics (typically model theoretic)  Decidable fragments of FOL  Closely related to Propositional Modal & Dynamic Logics – Provision of inference services  Sound and complete decision procedures for key problems  Implemented systems (highly optimised) 18
  • 19. OWL (Direct) Semantics • Model theoretic semantics. An interpretation consists of – A domain of discourse (a collection of objects) – Functions mapping  classes to sets of objects  properties to sets of pairs of objects – Rules describe how to interpret the constructors and tell us when an interpretation is a model. • Semantics described in terms of – Individuals in a domain of discourse – (Binary) Properties that relate individuals – Classes or collections of individuals • A class description is a characterisation of the individuals that are members of that class. 19
  • 20. OWL (Direct) Semantics • OWL has a number of operators for constructing class expressions. • These have an associated semantics which is given in terms of a domain: – Δ • And an interpretation function – I:concepts ! ℘(Δ) – I:properties ! ℘(Δ £ Δ) – I:individuals ! Δ • I is then extended to concept expressions. 20
  • 21. OWL Class Constructors Constructor Example Interpretation Classes Class: Human I(Human) and (Human and Male) I(Human) Å I(Male) or (Doctor or Lawyer) I(Doctor) [ I(Lawyer) not not(Male) Δ I(Male) {} {john mary} {I(john), I(mary)} 21
  • 22. OWL Class Constructors Constructor Example Interpretation some hasChild some Lawyer {x|9y.hx,yi2I(hasChild)^Æ y2I(Lawyer)} only hasChild only Doctor {x|8y.hx,yi2I(hasChild) ) y2I(Doctor)} min hasChild min 2 {x|#hx,yi2I(hasChild) ¸ 2} max hasChild max 2 {x|#hx,yi2I(hasChild) · 2} min hasChild min 2 Doctor {x|#{y|hx,yi2I(hasChild) ^Æ y2I (Doctor)} ¸ 2} max hasChild max 2 Lawyer {x|#{y|hx,yi2I(hasChild) ^Æ y2I (Lawyer)} · 2} 22
  • 23. OWL Axioms • Axioms allow us to add further statements about arbitrary concept expressions and properties – Subclasses, Disjointness, Equivalence, characteristics of properties etc. • An interpretation I satisfies an axiom if the interpretation of the axiom is true. – Axioms constrain the allowed models – They provide the additional “assumptions” about the way in which the domain should be interpreted. • I satisfies or is a model of an ontology (or knowledge base) if the interpretation satisfies all the axioms in the knowledge base. 23
  • 24. OWL Axioms Axiom Example Interpretation SubClassOf Class: Human I(Human) µ I(Animal) SubClassOf: Animal EquivalentTo Class: Man I(Man) = I(Human) Å I(Male) EquivalentTo: (Human and Male) Disjoint Disjoint: Animal, Plant I(Animal) Å I(Plant) = ; 24
  • 25. OWL Individual Axioms Axiom Example Interpretation Individual Individual: Sean I(Sean) 2 I(Human) Types: Human Individual Individual: Sean hI(Sean),I(Oscar)i2I(worksWith) Facts: worksWith Oscar DifferentIndividuals Individual: Sean I(Sean) ≠ I(Oscar) DifferentFrom: Oscar SameIndividuals Individual: BarackObama I(BarackObama) = I SameAs: PresidentObama (PresidentObama) 25
  • 26. OWL Property Axioms Axiom Example Interpretation SubPropertyOf ObjectProperty: hasMother I(hasMother) µ I(hasParent) SubpropertyOf: hasParent Domain ObjectProperty: owns 8x.hx,yi2I(owns) ) Domain: Person x2I(Person) Range ObjectProperty: employs 8x.hx,yi2I(employs) ) Range: Person y2I(Person) Transitive ObjectProperty: hasPart 8x,y,z. (hx,yi2I(hasPart) ^Æ hy,zi2I Characteristics: Transitive (hasPart)) ) hx,zi2I(hasPart) 26
  • 27. Models • An OWL Ontology doesn’t define a single model, it is a set of constraints that define a set of possible models • No constraints (empty Ontology) means any model is possible • More constraints means fewer models • Too many constraints may mean no possible model (inconsistent Ontology) 27
  • 28. Consequences • An ontology (collection of axioms) places constraints on the models that are allowed. • Consequences may be derived as a result of those constraints. • C subsumes D w.r.t. an ontology O iff for every model I of O, I(D) µ I(C) • C is equivalent to D w.r.t. an ontology O iff for every model I of O, I(C) = I(D) • C is satisfiable w.r.t. O iff there exists some model I of O s.t. I(C) ≠ ; • An ontology O is consistent iff there exists some model I of O. 28
  • 29. Reasoning • A reasoner makes use of the information asserted in the ontology. • Based on the semantics described, a reasoner can help us to discover inferences that are a consequence of the knowledge that we’ve presented that we weren’t aware of beforehand. • Is this new knowledge? – What’s actually in the ontology? 29
  • 30. Reasoning • Subsumption reasoning – Allows us to infer when one class is a subclass of another – B is a subclass of A if it is necessarily the case that (in all models), all instances of B must be instances of A. – This can be either due to an explicit assertion, or through some inference process based on an intensional definition. – Can then build concept hierarchies representing the taxonomy. – This is classification of classes. • Satisfiability reasoning – Tells us when a concept is unsatisfiable  i.e. when there is no model in which the interpretation of the 30 class is non-empty.
  • 31. Instance Reasoning • Instance Retrieval  What are the instances of a particular class C?  Need not be a named class • Instantiation  What are the classes that x is an instance of? 31
  • 32. Necessary and Sufficient Conditions • Classes can be described in terms of necessary and sufficient conditions. – This differs from some frame-based languages where we only have necessary conditions. • Necessary conditions – Must hold if an object is to be an instance of the class • Sufficient conditions – Those properties an object must have in order to be recognised as a member of the class. – Allows us to perform automated If it looks like a classification. duck and walks like a duck, then it’s a duck! 32
  • 33. Misconceptions: Disjointness • By default, primitive classes are not disjoint. • Unless we explicitly say so, the description (Animal and Vegetable) is not unsatifiable. • Similarly with individuals. The so-called Unique Name Assumption (often present in DL languages) does not hold, and individuals are not considered to be distinct unless explicitly asserted to be so. 33
  • 34. Misconceptions: Domain and Range • OWL allows us to specify the domain and range of properties. • Note that this is not interpreted as a constraint. • Rather, the domain and range assertions allow us to make inferences about individuals. • Consider the following: • ObjectProperty: employs Domain: Company Range: Person Individual: IBM Facts: employs Jim • If we haven’t said anything else about IBM or Jim, this is not an error. However, we can now infer that IBM is a Company and Jim is a Person. 34
  • 35. Misconceptions: And/Or and Quantification • The logical connectives And and Or often cause confusion – Tea or Coffee? – Milk and Sugar? • Quantification can also be contrary to our intuition. – Universal quantification over an empty set is true. – Sean is a member of hasChild only Martian – Existential quantification may imply the existence of an individual that we don’t know the name of. 35
  • 36. Misconceptions: Closed and Open Worlds • The standard semantics of OWL makes an Open World Assumption (OWA). – We cannot assume that all information is known about all the individuals in a domain. – Facilitates reasoning about the intensional definitions of classes. – Sometimes strange side effects • Closed World Assumption (CWA) – Named individuals are the only individuals in the domain • Negation as failure. – If we can’t deduce that x is an A, then we know it must be 36
  • 37. Annotations • OWL defines annotation properties. • These allow us to assert information about things (classes, properties, individuals) that don’t contribute to the logical knowledge. – No semantics (in the direct semantics) • Information not about the domain but about the modelling or description of the domain – e.g. DC style creation metadata • Annotations could also be used to support applications – e.g. labels (cf SKOS.....) 37
  • 38. Profiles • OWL Profiles describe subsets of the language that offer the potential for simpler/more efficient implementation • OWL EL – Roughly existential quantification of variables. • OWL QL – No class expressions in existential quantifications. – Query answering via rewrites into standard relational queries • OWL RL – Amenable to implementation using rule-based technologies. 38
  • 39. Lightweight Vocabularies • For many applications, lightweight representations are more appropriate. • Thesauri, classification schemes, taxonomies and other controlled vocabularies – Many of these already exist and are in use in cultural heritage, library sciences, medicine etc. – Often have some taxonomic structure, but with a less precise semantics. 39
  • 40. Concept Schemes • A concept scheme is a set of concepts, potentially including statements about relationships between those concepts – Broader Terms – Narrower Terms – Related Terms – Synonyms, usage information etc. • Concept schemes aren’t formal ontologies in the way that OWL ontologies are formal ontologies – Concepts are not intended to me interpreted as sets of things in the same way 40
  • 41. SKOS: Simple Knowledge Organisation • SKOS provides an RDF vocabulary for the representation of such schemes. • Designed with a focus on Retrieval Scenarios A. Single controlled vocabulary used to index and then retrieve objects B. Different controlled vocabularies used to index and retrieve objects • Mappings then required between the vocabularies – Initial use cases/requirements focus on these tasks • Not worrying about activities like Natural Language translation 41
  • 42. Knowledge Organisation SystemsThesaurus: Controlled vocabulary in which concepts are represented bypreferred terms, formally organised so that paradigmatic relationshipsbetween the concepts are made explicit, and the preferred terms areaccompanied by lead-in entries for synonyms or quasi-synonyms. Thesaurus Related Terms Taxonomy Hierarchy Authority File Preferred Terms Synonym Ring Equivalent Terms Controlled Vocabulary Collection of Terms Controlled vocabularies: designed for use in classifying or indexing documents and for searching them.
  • 43. Concepts vs Terms • SKOS adopts a concept-based (as opposed to term-based) approach • Concepts associated with lexical labels • Relationships expressed between concepts. • Possibility of expressing relationships between terms through SKOS-XL.
  • 44. SKOS Exampleanimals NT catscats UF domestic cats RT wildcats BT animals SN used only for domestic catsdomestic cats USE catswildcats Graphic: Antoine Isaac
  • 45. SKOS Model
  • 46. Labelling • Lexical Labels associated with Concepts – Preferred: one per language – Alternate: variants, – Hidden: mis-spellings • No domains stated, so usage possible on any resource. • Labels pairwise disjoint. • Label Extension (SKOS-XL) provides additional support for descriptions of labels and links between them – E.g. acronyms, abbreviations
  • 47. Documentation • A number of documentation properties • Not intended to be comprehensive • Extension points
  • 48. Semantic Relations • Hierarchical and Associative • Broader/Narrower • Loose (i.e. no) semantics – A publishing vehicle, not a set of thesaurus construction guidelines • Domain/Range restrictions on semantic relations • broader/narrower not transitive in SKOS
  • 49. Mapping Relations • Subproperties of Semantic Relations • Intended for cross-scheme usage – Although no formal enforcement
  • 50. SKOS and OWL • SKOS itself is defined as an OWL ontology. • A particular SKOS vocabulary is an instantiation of that ontology/schema – SKOS Concept is a Class, particular concepts are instances of that class • Allows use of OWL mechanisms to define properties of SKOS (e.g. the querying of the transitive closure of broader). –
  • 51. Transitivity of Semantic Relations • Broader/narrower not transitive in SKOS – Addition of broaderTransitive • Separate assertions from inferences • Thus can still query across transitive closure of broader. – User confusion with transitivity and inheritance.
  • 52. SKOS and OWL • SKOS and OWL are intended for different purposes. – OWL allows the explicit modelling/description of a domain  Support for inference, automated classificationm detection of inconsistencies etc. – SKOS provides vocabulary and navigational structure • Interactions between representations – Presenting OWL ontologies as SKOS vocabularies – Enriching SKOS vocabularies as OWL ontologies. – Use of SKOS as annotation vocabulary
  • 53. Hands On • Explore some example vocabularies in order to understand the semantics. • Investigate what the inferences are that we can draw, based on the class definitions and additional axioms in the ontologies. 53

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