Ontology Engineering

1. Ontology Engineering Nguyen Trung Lap

2. Contents • What is an ontology? • What is the usefulness of an ontology? • Ontologies languages • Ontologies development • Tool introduction • Labs – develop an ontology

3. What is an ontology? (1) • Philosophy: Ontology • Engineering: ontologies (count noun) • Investigating reality, representing it

4. What is an ontology? (2) • An ontology is an explicit description of a domain: • concepts • properties and attributes of concepts • constraints on properties and attributes • Individuals • An ontology defines • a common vocabulary • a shared understanding

5. What is an ontology? (3) • Ontology vs OOP • Ontology • Reflect the world • Structure • OOP • Reflect data and code • Behavior

6. Definitions • Definitions • Most quoted: “An ontology is a specification of a conceptualization” (by Tom Gruber, 1993) • “a formal specification of a shared conceptualization” (by Borst, 1997) • “An ontology is a formal, explicit specification of a shared conceptualization" (Studer et al., 1998) • An ontology is a hierarchically structured set of terms for describing a domain that can be used as a skeletal foundation for a knowledge base.(Swartout et al., 1997)

7. Definitions • “An ontology is a specification of a conceptualization” • Conceptualization: reality • Specification: language

10. What is the usefulness of an ontology? • Making, more or less precisely, the (dis-)agreement among people explicit • Enrich software applications with the additional semantics • Thus, practically, improving: computer-computer, computer-human, and human-human communication

11. Examples • Data(base) integration • Instance classification • Matching • Querying, information retrieval • Ontologies to improve NLP • Modeling • e-learning • Support for knowledge intensive applications. • Text extraction, decision support, resource planning, intelligent interfaces. • Knowledge repository structure.

12. Example: Job matching • Job seeker post: “.. work as programmer in HCMC” • Company offer: “.. software developer, Vietnam” • Keyword based: not matched

13. Example: Job matching • Job seeker post: “ work as programmer in HCMC” • Company offer: “ software developer, Vietnam” • Keyword based: not matched

14. Example: Job matching • Job seeker post: “ work as programmer in HCMC” • Company offer: “ software developer, Vietnam” • Simple knowledge base (an ontology): • Ontology-based matching: matched • Knowledge base • - Programmer is equal to software developer • - HCMC is located in Vietnam

15. Ontology Languages

16. Description logic - DL • DL is a family of formal knowledge representation languages • A Description Logic (DL) models concepts, roles and individuals, and their relationships.

17. DL • In DL, a distinction is drawn between the so-called TBox (terminological box) and the ABox (assertional box). • For example, the statement: • (1) Every employee is a person • belongs in the TBox, while the statement: • (2) Bob is an employee • belongs in the ABox.

18. DL Architecture

19. DL model • Let C and D be concepts, a and b be individuals, and R be a role

20. Resource Description Framework (RDF) • The W3C recommendation for semantic annotations in the Semantic Web

21. RDF • An RDF statement (or RDF triple) is of the form: • subject property object. • To represent RDF statements in a machine-processable way, RDF defines a specific extensible markup language (XML) syntax

22. Web Ontology LanguageOWL Semantic web components

23. Stack of language • XML • Surface syntax, no semantics • XML Schema • Describes structure of XML documents • RDF • Data model for “relations” between “things” • RDF Schema • RDF Vocabulary Definition Language • OWL • A more expressive Vocabulary Definition Language

24. Design goal for OWL • Shareable • Changing over time • Interoperability • Inconsistency detection • Balancing expressivity and complexity • Ease of use • Compatible with existing standards • Internationalization

25. Requirement for OWL • Ontologies are object on the Web • with their own meta-data, versioning, etc... • Ontologies are extendable • They contain classes, properties, data-types, • range/domain, individuals • Equality (for classes, for individuals) • Classes as instances • Cardinality constraints • XML syntax

26. OWL Profiles • OWL Lite • Classification hierarchy • Simple constraints • OWL DL • Maximal expressiveness • While maintaining tractability • Standard formalization in a DL • OWL Full • Very high expressiveness • Losing tractability • All syntactic freedom of RDF (self-modifying)

27. OWL Profiles • OWL Lite • (sub)classes, individuals • (sub)properties, domain, • range • conjunction • (in)equality • (unqualied) cardinality 0/1 • datatypes • inverse, transitive, • symmetric properties • someValuesFrom • allValuesFrom • OWL DL • Negation • Disjunction • (unqualied) Full • cardinality • Enumerated classes • hasValue • OWL Full • Meta-classes • Modify language

28. OWL 2 structure http://www.w3.org/TR/owl2-overview/

29. OWL 2 • OWL 2 is a language for expressing ontologies. • OWL 2 is not a programming language: OWL 2 is declarative • OWL 2 is not a database framework • Database: close-world assumption • OWL : open-world assumption

30. Modeling Knowledge: Basic Notions • Axioms: the basic statements that an OWL ontology expresses • Entities: elements used to refer to real-world objects • Expressions: combinations of entities to form complex descriptions from basic ones

31. OWL syntax via example • Example, model by introducing a person • Class and instance

33. Class hierarchy

36. Property hierarchy

37. Domain and range

38. Individual

39. Data type

40. Complex class

41. Union class

42. Restriction

43. Need to remember the syntax? • NO • Tool will support you

44. SWRL (1) • SWRL – semantic web rule language • Rules are if-then clauses. • New knowledge is added only if a particular set of statements is true

45. SWRL(2) • Combine ontologies and rules • Ontologies: OWL-DL • Rules: RuleML

46. Why SWRL? • Improve the ontology’s expressivity • Cover some limitations of OWL • Although expressivity comes with a price • Decidability • Rule can be reused • It is easier to read and write rules with SWRL • Suitable for policy definition

47. SWRL syntax • Atom <- C(i)|D(v)|R(i; j)|U(i; v) |builtIn(p; v1,..., vn) |i = j | i # j • C = Class D = Data type • R = Object Property U = Data type Property • I, j = Object variable names or Object individual names • v1… vn = Data type variable names or Data type value names • p = Built-in names

48. SWRL Syntax (2) • a <- b1,…, bn where, • a : head (an atom) • bi: body (all atoms) • ‘,’ mean and • A SWRL knowledge base (k) is defined as follows: • k = (∑, P) where, • ∑ = Knowledge base of OWL • P = A finite set of rules

49. SWRL Example • Person(?x), hasChild(?x, ?y) -> hasSpouse(?x)

50. Ontology reasoning • Reasoning over a first-order logical theory • Main (`standard') reasoning tasks for the OWL ontologies: • consistency of the ontology • concept (and role) consistency • concept (and role) subsumption • instance checking • instance retrieval • query answering