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Applying Belief Change to Ontology Evolution

Applying Belief Change to Ontology Evolution. Giorgos Flouris. PhD Student Computer Science Department University of Crete fgeo@csd.uoc.gr. PhD Thesis Summary. Research Assistant Institute of Computer Science FORTH fgeo@ics.forth.gr. ISWDS 05 07/11/05. Part  Overview (“Elevator Talk”).

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Applying Belief Change to Ontology Evolution

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  1. Applying Belief Change to Ontology Evolution Giorgos Flouris PhD StudentComputer Science DepartmentUniversity of Cretefgeo@csd.uoc.gr PhD Thesis Summary Research AssistantInstitute of Computer ScienceFORTHfgeo@ics.forth.gr ISWDS 0507/11/05

  2. Part Overview(“Elevator Talk”)

  3. Ontology Evolution and Belief Change • We propose a different viewpoint on ontology evolution: • Addressing the problem of ontology evolution using techniques from belief change • In particular: • AGM theory of contraction • In ontologies represented using some DL or OWL flavor

  4. Logics (under Tarski’s model) AGM-compliantlogics AGM Class Summary of Results DLs(CVA) Base-AGM-compliantlogics OWL DLs(OVA) DLs

  5. Part Research Description

  6. Ontology Evolution:Definition and Importance • Ontology evolution is the process of modifying an ontology in response to a certain change in the domain or its conceptualization • Main reasons for ontology evolution: • Dynamic domains • Change in users’ needs or perspective • New information (previously unknown, classified or unavailable) that improves the conceptualization • Errors during original conceptualization • Ontology dependency • …

  7. Current Approaches User: , , ,  System: ,  Output Ontology Input Ontology Ontology Evolution Change Capturing  “penguins can’t fly” Penguin⊑Fly Change Representation Semantics of Change  Add_IsA(…) Implementation Change Propagation  Success Validation  Fail

  8. Limitations • Main limitations of current approaches: • Manual or semi-automatic approaches • Too many operators (complex and atomic) • No formal semantics • Cause problems: • Automated agents and systems • Scalability • Formal properties unknown • Bottleneck for current research

  9. Output Ontology Proposed Approach Input Ontology User:  System: , , , ,  Ontology Evolution Change Capturing  “penguins can’t fly” Penguin⊑Fly Change Representation Semantics of Change  Add_IsA(…) Implementation Change Propagation  Success Validation  Fail

  10. Why Belief Change?(1/2) • Knowledge should be up-to-date: • Keeping KBs up-to-date: belief change • Keeping ontologies up-to-date: ontology evolution • Ontology evolution can be viewed as a special case of belief change: • View belief change techniques, ideas, intuitions, results, algorithms and methods under the prism of ontology evolution • We address ontology evolution using belief change

  11. Why Belief Change?(2/2) • Belief change properties: • Mature • Formal • Automatic • Addresses important issues that have not been considered in ontology evolution: • Revision and Update • Revision and Contraction • Postulations vs Explicit Constructions • Foundational vs Coherence Theories • Principle of Minimal Change • Principle of Primacy of New Information

  12. Difficulties and Methodology • Belief change techniques are generally targeted at classical logic: • Their assumptions fail for DLs and other ontological languages • Cannot be directly used for such logics • But: the underlying intuitions are applicable • Belief change techniques need to be migrated to the ontology evolution context • PhD, Phase 1: • Set the foundations for future work on the subject • Very abstract, long-term and ambitious goal

  13. A More Specific Approach:the AGM Theory • For the purposes of this PhD, we restricted ourselves to deal with: • The most influential belief change theory (AGM theory) • The most fundamental operation (contraction) • The most promising languages for ontological representation (DLs and OWL) • PhD, Phase 2: • Study the applicability of the AGM theory of contraction in DLs and OWL

  14. AGM Theory • AGM theory (Alchourron, Gärdenfors, Makinson): • The most influential approach in belief change • Contraction: • The most fundamental operation for theoretical purposes • Deals with the removal of knowledge from a KB • Main contribution: 6 AGM postulates that determine whether a contraction operator behaves “rationally” • AGM theory is based on certain assumptions on the underlying logic, so, as usual: • Intuitions applicable in ontologies • Postulates and results not applicable in ontologies

  15. AGM-Compliance • Dropped the AGM assumptions and considered the class of logics studied by Tarski: • Very general class of logics (that contains DLs) • We generalized the AGM theory (and postulates) to be applicable to Tarski’s class • Noticed that only some of the logics in this class admit an operator satisfying the generalized postulates (i.e., a “rational” operator): • Termed AGM-compliant logics (3 characterizations)

  16. Logics (under Tarski’s model) AGM-compliantlogics AGM Class Results(AGM-Compliance)

  17. Further Results • Connection with lattice theory: • Every logic can be described by a lattice • AGM-compliance can be determined by the lattice’s structure • Connection with the foundational model: • AGM theory based on the coherence model • There are logics in which a “foundational AGM theory” can be applied • Termed base-AGM-compliant logics (2 characterizations)

  18. Logics (under Tarski’s model) AGM-compliantlogics AGM Class Results(Base-AGM-Compliance) Base-AGM-compliantlogics

  19. AGM-Compliance and DLs • Studied DLs (two types) • CVA (Closed Vocabulary Assumption): allows the description of the ontological signature using DL axioms • OVA (Open Vocabulary Assumption): ignores the signature because it cannot be described using DL axioms • DLs (CVA): non-AGM-compliant • DLs (OVA): some are AGM-compliant, some are not • Introduced results, heuristics, rules of thumb • OWL (different flavors, CVA or OVA, annotation features, owl:imports): all non-AGM-compliant

  20. Logics (under Tarski’s model) AGM-compliantlogics AGM Class Results(AGM-Compliance and DLs) DLs(CVA) Base-AGM-compliantlogics OWL DLs(OVA)

  21. Partial List of DLs (OVA)

  22. Conclusion • Phase 1: • Proposed the study of ontology evolution from a different perspective, using belief change ideas and terminology • Phase 2: • Focused on the AGM theory of contraction • Determined its applicability to DLs and OWL

  23. Future Work • Study other belief change approaches • Connection of AGM-compliance with other AGM-related results: • The operation of revision • Levi identity • Representation theorems • The development and/or implementation of a specific algorithm for integration into ontology evolution tools

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