1. Lecture Notes University of Birzeit 2nd Semester, 2010 Advanced Artificial Intelligence (SCOM7341) Ontology Part 4 Stepwise Methodologies Dr. Mustafa Jarrar mjarrar@birzeit.eduwww.jarrar.info University of Birzeit

2. Reading Material 0) Everything in these slides 1) Jarrar, M.: Towards Methodological Principles for Ontology Engineering. PhD thesis, Vrije Universiteit Brussel (2005). See http://www.jarrar.info Only chapter 4 (Section 1-3) 2) Mustafa Jarrar: Towards Effectiveness and Transparency in e-Business Transactions, An Ontology for Customer Complaint Management . http://www.jarrar.info/publications/mjarrar-CCFORM-chapter.v08.pdf This is a case study

3. Methodology Let’s discuss form where to start, if you want to build an ontology for: E-government E-Banking E-Health Bioinformatics Multilingual search engine … What are the phases of the ontology development life cycle? taking into account that Ontologies might be built collaboratively by many people.

5. Which languages and tools should be used in which circumstances, and in which order?

6. What about issues of quality control and resource management?

7. Many Methodologies exist ! But non is good! Because each project/application/domain is different, and the background of the people involved are also different.

9. 1- Purpose and Scope There is no correct ontology of a specific domain An ontology is an abstraction of a particular domain, and there are always alternatives. What is included in this abstraction should be smartly determined by: the use to which the ontology will be put, such as: – Interoperability between systems. – improve quality Search. – Communication between people and organizations (important). by future extensions that are already anticipated.

12. For what types of questions should the ontology provide answers?

13. Who will use and maintain the ontology? And how?Be carful with the ontology usability - reusability trade-off

14. 2- Building the Ontology 2.1- Ontology Capture – Identify key concepts and relationships. – Produce clear text definitions for these concepts (glosses, etc.). – Identify terms that refer to these concepts – Reach Consensus (Consensus is an indication of correctness). 2.2- Ontology Coding/Specification/Characterization – Explicit representation of the “conceptualization” in some formal language.

17. Extract candidate relations, and/or subsumptions.

18. Generate glosses.• Grouping: Structure terms loosely into work areas/topics – Provisionally categorize them for inclusion or exclusion (purpose and scope) – Keep notes of these decisions. – Group similar terms and potential synonyms together.

20. especially: use words and modeling primitives in a consistent manner (e.g. Type, role, entity, instance, relationship...)

21. Work Areas: Start with the most basic/important

22. Define the most basic (i.e. important) terms first in each work area before moving to more abstract or more specific terms.

23. Semantic overlap with others must be right in the first place, otherwise lot of redundant re-working.

24. Terms: Produce definitions in a middle-out fashion

26. Opinions differ on whether it is more efficient to do this in a top-down or a bottom-up fashion.

27. Ensure that hierarchy is indeed a taxonomy:

28. If A subsumes B, then every instance of A must also be a subsume B (compatible with semantics of rdfs:subClassOf

31. There is a methodological tension here between generality and specificity:

32. Flexibility (inheritance to subsumptions)

34. Which properties should be unique, mandatory, disjunctions, restricted values…etc.

35. Relational Characteristics

37. Country: Palestine

38. Person: Arafat

41. How to facilitate reach agreement?• Produce a natural language text definitions • Ensure consistency with terms already in use – use existing thesauri and dictionaries – avoid introducing new terms in the definitions • Indicate relationships with other commonly used terms – synonyms, variants, such referring to different dimensions • Give examples

42. Integrating Existing Ontologies • Check overlap with existing ontologies • Establish formal links – Produce mappings to existing concept definitions – Import and extend existing ontologies • Avoid re-inventing the wheel!

43. Ontology Evaluation Several Type of evaluations: Usability Evaluation: Validate whether the ontology produced is consistent with and meets the requirements specification. Syntax evaluation: Validate whether the ontology well-formed w.r.t the used language. Logical evaluation: Validate whether the ontology has axioms contradicting or implying each other. Ontological Evaluation: Validate whether the ontology has concepts that should be instances, sub-concepts that should be roles, etc. (The OntoClean methodology is very good for this evaluation)

44. Check for Implications and Contradictions Some tools exist to automatically detect logical correctness (contradictions and implications), depending on the used ontology language (Such as ORM: DogmaModeler, OWL: Racer)

45. Some Guidelines Clarity: The ontology engineer should communicate effectively with the domain experts (= ask the rightquestions): – Natural language definitions. – Give examples, alternative, and contradictions, elicit knowledge – emphasize distinctions Coherence: The ontology should be internally consistent Syntactically correct. Logical consistent. Ontologically consistent. Extensibility: modularize the ontology in a way it is easy to build, understand, and maintain. What should be in a module? Reusability and Usability: be innovative to tradeoff this smartly.

46. Ontology Modularization