1. 11 -1 Lecture 11 Ontology Engineering Topics –Basics –Representation and Reasoning of Ontology –General Ontology Languages –Web-centric Ontology Languages –Ontology Development –Applications

2. 11 -2 Basics An ontology is a formal, explicit specification of a shared conceptualization about important concepts and axioms of a domain –Concepts (e.g., computer_scientist ) and Attributes (e.g., major ) –Axioms relationships among scientists (e.g., colleague ) logics (e.g., a computer scientist knows more of computers than others ) An ontology provides underlying semantics for intelligent systems, e.g., agents

3. 11 -3 Basics Characteristics –Dictionary about domain concepts –Structure of domain concepts –Machine-processable –Sharing –Re-use –Reliability

4. 11 -4 Basics Example ontology about scientist: concepts

5. 11 -5 Basics Example ontology: relation axioms and attributes

6. 11 -6 Basics Example ontology about query (instances)

7. 11 -7 Representation and Reasoning of Ontology Requirements for ontology language design –Easy to understand and use (based on common KR idioms) – Formally specified and of “adequate” expressive power – Possible to provide automated reasoning support –Compatible with existing standards

8. 11 -8 Representation and Reasoning of Ontology Why automatic reasoning –Querying class and instance data w.r.t. ontologies Determine if set of facts are consistent w.r.t. ontologies – consistency problem Determine if individuals are instances of ontology classes – classification problem Retrieve individuals/tuples satisfying a query expression – axiom reasoning problem Check if one class subsumes (is more general than) another w.r.t. ontology – subsumption problem

9. 11 -9 Representation and Reasoning of Ontology Why automatic reasoning –Design and maintenance of ontologies Check class consistency and compute class hierarchy –Integration of ontologies Assert inter-ontology relationships Compute integrated class hierarchy/consistency

10. 11 -10 Representation and Reasoning of Ontology General ontology languages –FOL (First-order Logic) –DL (Description Logics) –Frame logic Web-centric ontology languages –XML (eXtensible Markup Language, W3C ) W3C: World Wide Web Consortium –RDF (Resource Description Framework, W3C ) –DAML (DARPA Agent Markup Language, US ) + OIL (Ontology Inference Layer, or Ontology Infrastructure Language, European OntoKnowledge ) OWL (Ontology Web Language, W3C )

11. 11 -11 General Ontology Languages - DL DL is a (decidable) subset of First-order logic The basic building blocks are concepts, roles and individuals (instances). Concepts describe the common properties of a collection of individuals and can be considered as classes, interpreted as sets of objects. Roles are interpreted as binary relations between objects, interpreted as attributes. A number of language constructs (such as intersection, union, role quantification, etc.) that can be used to define new concepts and roles.

12. 11 -12 General Ontology Languages - DL Syntax and semantics of example description logic Concept definitions –Father = Man   has-child.Human –Father: concept; has-child: role Axioms –Favourite.Brewery   drinks.Beer Model: a denotational semantic which satisfies all definitions and axioms Example sentence - some happy father: Man  (  has-child.Allen)  (  has-child.Alice)  (  has-child.Nice)

13. 11 -13 General Ontology Languages - DL Reasoning of DL –Reasoning tasks: subsumption, classification, satisfiability, instance checking, role checking, etc. –Subsumption: Is concept C more general than concept D? –Classification: Which concept does an instance belong to? –Satisfiability: Is there a model for the ontology? –Consistency: Is concept C consistent with the ontology? –Role checking: Is role between concepts C and D? –Retrieval The reasoning mechanism is decidable.

14. 11 -14 General Ontology Languages - Frame Frame logic –Frame-based systems –Network-based natural structure Major reasoning tasks: –Subsumption –Anchoring (Classification) –Inheritance: Finding definitions for specific concepts –Axioms reasoning: Finding relationships among concepts –Attachment procedure reasoning Representation and reasoning can be formalized by description logics

15. 11 -15 General Ontology Languages - Frame Example: Frame Chair1 {// Frame name: Chair1 Slot isa // Slot name: isa for taxonomy relation Value Chair// Value facet of Slot isa: Chair Slot Left-Of// Slot name: Left-Of for binary relation Value Chair2 Slot Legs// Slot name: Legs an attribute Value 4 If-modified Stability-Check // procedure attachment to Slot Legs Slot Arm Value Yes Slot Material Value Wood } Stability-Check: If Legs ≦ 2 Then Chair1 is unstable; If Legs = 3 Then do Angle-spacing-check.

16. 11 -16 General Ontology Languages - Frame Equivalent description logics Chair1 = Chair  (  Left-Of.Chair2)  (  Legs.4)  (  Arm.Yes)  (  Material.Wood)  (  Left-Of. check-on- number-restriction) Equivalent First-order logic Isa(Chair1, Chair); Left-Of(Chair1, Chair2); Legs(Chair1, 4); Arm(Chair1, Yes); Material(Chair1, Wood); Stability-Check:  x Legs(Chair1, x)  Less(x, 3) → Unstable(Chair1)  x Legs(Chair1, x)  EQ(x, 3) → Angle-Spacing-Check(Chair1)

17. 11 -17 Web-centric Ontology Languages - XML HTML ⊂ XML ⊂ SGML (Standard Generalized Markup Language ) In XML, tags are not fixed - one can invent new tags to structure the information in a web page DTD (Document type definition) defines the legal building blocks of an XML document; it defines the document structure with a list of legal elements

18. 11 -18 Web-centric Ontology Languages - XML XML schema express shared vocabularies and allow machines to carry out rules made by people.; they provide a means for defining the structure, content and semantics of XML documents DTD ⊂ XML schema XSL (eXtensible Stylesheet language) for different presentation styles of XML documents XML is considered to be the basis for all semantic web languages - the “machine code” of the new generation web

19. 11 -19 Web-centric Ontology Languages - RDF RDF was built on URl (uniform resource identifier)+ XML Represent information about resources in the Web in terms of subject-predicate-object –Resource: subject, e.g. http://www.example.org/index.html –Property: predicate, e.g., creator –Property value: object, e.g., John Smith RDF schema allows anyone to write their own name-space document (a ‘ schema ’ ); this defines properties and classes in some application domain Weak in describing Web resources in sufficient detail

20. 11 -20 Web-centric Ontology Languages - RDF RDF example " The Author of ‘http://scom.hud.ac.uk/scomtlm/Artform/plannin g.html’ is Lee McCluskey.” IN RDF: Lee McCluskey Resource, Property, Values can all have URI ’ s

21. 11 -21 Web-centric Ontology Languages – DAML+OIL DAML+OIL is “layered” on top of RDFS – RDFS based syntax and ontological primitives (e.g., subclass ) – Richer set of primitives (e.g., transitivity, cardinality,... ) Describes structure of domain in terms of Classes and Properties –Various constructors provided for building class and axiom expressions – E.g., Person subclass of Animal whose parents are all Persons Uses RDF for class/property membership assertions (ground facts) – E.g., john instance of Person; instance of parent

22. 11 -22 Web-centric Ontology Languages – DAML+OIL DAML+OIL supports DL-based semantics and reasoning – Formal properties well understood (complexity, decidability) – Known reasoning algorithms – Implemented systems (highly optimized) Expressive power determined by – Kinds of constructor provided – Kinds of axiom allowed

23. 11 -23 Web-centric Ontology Languages - DAML+OIL Examples of DAML+OIL Class Constructors Constructor DL Syntax Example intersectionOf C1  …  Cn Human  Male unionOf C1  …  Cn Doctor  Lawyer complementOf  C  Male oneOf {x1,…, xn} {john, mary} toClass  P.C  hasChild.Doctor hasClass  P.C  hasChild.Lawyer hasValue  P.{x}  citizenOf.{USA} minCardinalityQ ≥nP.C ≥2hasChild.Lawyer maxCardinalityQ≤nP.C ≤1hasChild.Male cardinalityQ =nP.C =1hasParent.Female

24. 11 -24 Web-centric Ontology L anguages - DAML+OIL Examples of DAML+OIL Axiom Constructors Axiom DL Syntax Example subClassOf C1 ⊂ C2 Human ⊂ Animal  Biped sameClassAs C1 ≡ C2 Man ≡ Human  Male subPropertyOf P1 ⊂ P2 hasDaughter ⊂ hasChild samePropertyAs P1 ≡ P2 cost ≡ price sameIndividualAs {x1} ≡ {x2} {President_Bush} ≡ {G_W_Bush} disjointWith C1 ⊂  C2 Male ⊂  Female differentIndividualFrom {x1} ⊂  {x2} {john} ⊂  {peter} inverseOf P1 ≡ P2- hasChild ≡ hasParent -

25. 11 -25 Web-centric Ontology Languages - DAML+OIL DAML+OIL example Define a product number's domain and range.. Product Number <rdfs:range rdf:resource= "http://www.w3.org/2000/10/XMLSchema#nonNegativeInteger"/> ”Availability" is a sort of enumerated type.. In stock Back ordered Special order

26. 11 -26 Ontology Development Ontology construction, publishing, browsing, evaluation and maintenance Ontology construction –Manually write ontology in Frame logic, XML+XML schema, RDF+RDF schema, DAML+OIL, DL, or FOL –Use tools to construct and edit ontology Protégé 2000 (Stanford, US) – lots of plug-ins OntoEdit (Ontoprise, Germany) - Frame logic OilEd (Manchester, UK) for DAML+OIL

27. 11 -27 Ontology Development Ontology construction –Automatic ontological construction Multi-level clustering to find ontological concepts Ontology evaluation and maintenance –Tools to do ontology V&V, customization, extension, specialization, generalization, and integration

28. 11 -28 Applications Knowledge engineering and management –Support reasoning and learning of knowledge base –Support verification and validation of knowledge base User modeling –Support the capture and development of user domain proficiency, mental states and interests Multimedia search and retrieval –Facilitate the development of semantics for multimedia –Facilitate semantic-based search and retrieval

29. 11 -29 Applications Query processing –Support the comprehension of Query intention and focus Agent-based computing –Facilitate richer service descriptions that facilitate agent reasoning and learning –Facilitate knowledge level agent communication E-learning –Facilitate the construction of student model (study progress) –Support automatic test questions generation

30. 11 -30 Applications E-commerce –Facilitate communication among parties (e.g., buyer–seller) Semantic Web –Support domain concepts – directed focused Web search –Semantics-based Web portal (retrieval and service ) Ubiquitous computing –Facilitate semantic level communication among all computing devices