ONTOLOGIES Helena Sofia Pinto ( sofia.pinto@dei.ist.utl.pt )

1. ONTOLOGIES Helena Sofia Pinto (sofia.pinto@dei.ist.utl.pt) February 24, 2006

2. Summary • Knowledge sharing • Ontologies • What is an ontology? • Kinds of ontologies • How are ontologies built? • Kind of life cycle • Ontology building processes • Ontology building tools • Application areas and challenges • Where is the research? February 24, 2006

3. Knowledge Sharing • Problem: • The cost of knowledge based systems • Building the knowledge base from scratch • KB Components • Medical diagnosis and medical tutoring • Vocabulary definition: disease, organ, pathogenic agents (bacteria, virus, etc), kinds of bacteria (coli, coccos – estreptococcos, estaphilococcos -, etc) etc. – ontology • Electronic diagnosis vs medical diagnosis • Raise hypothesis, test, refine, etc. – problem solving method February 24, 2006

4. Knowledge Sharing • Solution: • Reuse and Sharing of knowledge • Translation of knowledge bases between different KR languages • Arbitrary differences among systems belonging to the same family • Remote access to the knowledge base of another system • Meaning of what is shared: Lack of consensus about vocabulary February 24, 2006

5. What is an ontology? • Capture the static knowledge in a given domain that is accepted and sharable across applications and groups • Defs: • An explicit formal specification of a shared conceptualization • a vocabulary of terms and some specification of their meaning February 24, 2006

6. What is an ontology? • Set of symbols (concepts) + hierarchy (organized) + some specification of their meaning (restrict the possible interpretations for those symbols) • Concepts are defined by their relations with other concepts O1 O2 rel1 xpto xv xpt1 xv1 xpt2 ist xv2 February 24, 2006

7. What is an ontology? • Distinction ontology/KB • different role played by represented knowledge • ontologies - k. +/- consensual of a community • process, activity, resource • kb - k. specific of a particular problem being solved, changes with inference • activities of a particular enterprise; actual processes, activities, costs, resources used to build or produce a particular product; estimate of resources inferred to be needed to satisfy an order February 24, 2006

8. What is an ontology? • Depend on the application that powered its construction • Same domain/ different tasks • a large number of common concepts • differently defined: • different levels of detail (class, relation, etc.) • capturing different points of view (structural point of view, functional point of view, etc.) • different levels of granularity • There is no “The Ontology!” – genuine alternatives!! • Not to the philosophers February 24, 2006

9. Kinds of ontologies • representation or meta- • capture the representation primitives in a KR family or paradigm (Frames: class, instance, relation -slots and facet-, function, etc.) • general or upper • capture very general notions applicable across domains (Time: time-point, time-range, duration, overlaps, before, after, etc.) • domain • specific of a particular domain (Chemical elements: elements, non-reactive elements, helium, non-metals, carbon, etc.) • others... February 24, 2006

10. Kinds of ontologies D. McGuinness – Ontologies Come of Age February 24, 2006

11. Origin • An Ontology that describes the processes of the Central Dogma of Molecular Biology for prokaryotic organisms. • Formalized and can be used by an inference engine to answer user questions February 24, 2006

12. Origin: Processes February 24, 2006

13. Origin Entities February 24, 2006

14. Origin Relations (1) February 24, 2006

15. Origin: Relations (2) February 24, 2006

16. Origin: Roles February 24, 2006

17. Origin: Transcription February 24, 2006

18. Origin: Transcription, subactivities February 24, 2006

19. Origin: Activities February 24, 2006

20. How are they built? • General Process • Life cycle • Sub-Processes: • from scratch • by means of reuse: • integration • merge February 24, 2006

21. Especificação Conceptualização Formalização Implementação Manutenção General process Aquisição de Conhecimento Avaliação Documentação February 24, 2006

22. A1 A2 A3 Evolutivo Life cycle • Prototipização evolutiva A1 A2 A3 A1 A2 A3 Cascata Iterativo February 24, 2006

23. Methodologies to build from scratch • There are a few methodologies to build ontologies from scratch • None of existing methodologies from scratch is widely accepted • It is still more of a craft than an engineering task February 24, 2006

24. Methodologies to build from scratch • Most representative methodologies are: • TOVE methodology [Gruninger, Fox 1995] • ENTERPRISE methodology [Uschold, King 1995] • METHONTOLOGY [Fernández, Gómez-Pérez, Sierra 1999] February 24, 2006

25. TOVE activity corresponds to Capture motivating scenarios and formulate informal competency questions Specification Specify terminology, formulate formal competency questions and specify axioms and definitions in FOL Conceptualization, Formalization and Implementation Evaluate competency and completeness Evaluation TOVE February 24, 2006

26. ENTERPRISE activity corresponds to Identify purpose and scope Specification Capturing knowledge Knowledge Acquisition and Conceptualization Coding Formalization and Implementation Evaluate Evaluation Document Documentation ENTERPRISE February 24, 2006

27. Techniques • Knowledge acquisition: • brainstorming, interviews, questionnaires, text analysis, mind maps • experts, books, norms, etc. • Conceptualization: • middle-out, grouping, glossary of terms, concept classification trees • Formalization: • intermediate tabular representations (concept dictionary, table of binary relations, etc.) February 24, 2006

28. Ontology evaluation • One needs to guarantee quality • technical evaluation: judge ontologies, their software environment and documentation against a framework: • consistency, completeness, conciseness, etc. • user assessment: judge from the user point of view the usability and usefulness of ontologies, their software environment and documentation when they are reused or shared in applications • understandability, technically evaluated, portable, etc. [Gómez-Pérez, Juristo, Pazos 1995] [Gómez-Pérez 1999] February 24, 2006

29. Ontology evaluation • ONTOCLEAN: analyze hierarchical taxonomy using philosophical principles. • Aims: • assure that instances do not violate class properties • assure consistent hierarchical structure [Guarino, Welty, 2001, 2002] February 24, 2006

30. Ontology building tools • Most important tools freely available: • PROTÉGÉ, http://protégé.stanford.edu/ • Ontolingua Server, http://WWW-KSL-SVC.stanford.edu:5915 • OntoEdit, http://ontoprise.de/products/ontoedit/ • KAON, http://kaon.semanticweb.org/ [Duineveld, Stoter, Weiden, Kenepa, Benjamins 1999] Some provide help to identify similar concepts (merge): PROTÉGÉ (PROMPT, ex-SMART) Ontolingua Server (Chimaera) February 24, 2006

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