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Ontology Language Handling Imperfection: Enhancing Web Modelling and Uncertainty in Web Modelling

Explore the application of Description Logic and Fuzzy Logic models in Web Modelling, including Bayesian networks and user preference modeling. Learn about handling imperfections and uncertainty in ontology languages. Discover methods for global preference aggregation and top-k answers. Join A. Eckhardt and P. Vojtáš on this insightful journey into enhancing web modelling.

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Ontology Language Handling Imperfection: Enhancing Web Modelling and Uncertainty in Web Modelling

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  1. Towards ontology language handling imperfection Alan Eckhardt, Peter Vojtáš WIKT 2006

  2. DL and Web modelling (časť prezentácie z ISWC06) • DL basis for OWL • Value restriction has to be reconsidered • EL DL sufficient for applications • EL querying in poly time • Extension to user preference modelse.g. a user looking for a cheap hotel close to a beach • Aggregation of particular attribute preferences to global preference • Top-k answers A.Eckhardt, P. Vojtáš

  3. Uncertainty in Web Modelling • Probabilistic models in SW • Fuzzy logic models SW • Fuzzy EL - fuzzy concepts • - crisp roles • - fuzzy aggregation • Enables concepts like @( price.cheap, distance.close) • Problem of learning @ for each user A.Eckhardt, P. Vojtáš

  4. Description logic – classical AL* interpretation EL A.Eckhardt, P. Vojtáš

  5. Description logic – full fuzzification – U. Straccia syntax Fuzzy interpretation Which t-norm t-conorm? A.Eckhardt, P. Vojtáš

  6. Bayesian networks (slide z článku s M. Vomlelovou) • Graph represents relations between variables • Conditional probabilities • Typical use: • We know: A=2, C=2 • We ask for: P(B|A=2,C=2) A.Eckhardt, P. Vojtáš

  7. From FILP, IGAP to BN (slide z článku s M. Vomlelovou a T. Horváthom) A.Eckhardt, P. Vojtáš

  8. Bayesian EL DL and others • Concepts are r. v. over preference scale • Roles are crisp / certain • Aggregation = combination function • Integrated with fEL, classical EL , … A.Eckhardt, P. Vojtáš

  9. Conclusion • Sharing ideas • Ideas supported by an analogy working in LP and simple experiments • Further development of formal model experiments OWL extension • Questions, comments, … A.Eckhardt, P. Vojtáš

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