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Creating, Maintaining, and Integrating Understandable Knowledge Bases

Creating, Maintaining, and Integrating Understandable Knowledge Bases. Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 650-723-9770 dlm@ksl.stanford.edu Joint work with

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Creating, Maintaining, and Integrating Understandable Knowledge Bases

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  1. Creating, Maintaining, and Integrating Understandable Knowledge Bases Deborah L. McGuinness Associate Director and Senior Research Scientist Knowledge Systems Laboratory Stanford University Stanford, CA 94305 650-723-9770 dlm@ksl.stanford.edu Joint work with Steve Wilder Jessica Jenkins Honglei ZengZhou Qing Gleb Frank

  2. Research Directions (Formation and Evolution) • Analysis of KBs • Deducible contradictions • Possible contradictions • Incomplete specifications • Potential modeling issues • Merging KBs • Special Purpose Representation & Reasoning • Defaults • Part-Whole • Explanation • Structural Similarities/Differences • Hybrid Reasoning Environments • Disjointness exploitation • Object Markup(in coordination with DAML) for • Object presentation (salient features) • Explanation • More effective usage

  3. Chimaera – A Merging and Diagnostic Ontology Environment Web-based tool utilizing the KSL Ontolingua platform that supports: • merging multiple ontologies found in distributed environments • analysis of single or multiple ontologies • attention focus in problematic areas • simple browsing and mixed initiative editing

  4. Our KB Analysis Task • Review KBs that: • Are developed using differing standards • May be syntactically but not semantically validated • May use differing modeling representations • May have different purposes • May be incomplete • Produce KB logs (in interactive environments) • Identify provable problems • Suggest possible problems in style and/or modeling • Are extensible by being user programmable

  5. Integration • Interactively impact agenda • Analysis of glycolysis on sabina’s input for example • Synonym list suggestions • Update “must” or “should” or possible portions of agenda • Disjointness options • Explain suggestions • Present results of modifications (with explanations) • Batch mode analysis

  6. How KB Merging Tools Can Help • Combine input KBs with name clashes • Treat each input KB as a separate name space • Support merging of classes and relations • Replace all occurrences by the merged class or relation • Test for logical consistency of merge (e.g. instances/subclasses of multiple disjoint classes) • Actively look for inconsistent extensions • Match vocabulary (using syntax and semantics) • Find name clashes, subsumed names, synonyms, acronyms, structural similarity, necessary and sufficient conditions for subsumption, etc. • Focus attention • Portions of KB where new relationships are likely to be needed E.g., sibling subclasses from multiple input KBs • Derive relationships among classes and relations • Disjointness, equivalence, subsumption, inconsistency, ...

  7. Chimaera I Usage • HPKB program – analyze diverse KBs, support KR novices as well as experts • Cleaning semi-automatically generated simple KBs • Browsing and merging multiple controlled vocabularies (e.g., internal vocabularies and UN/SPSC (std products and services codes)) • Reviewing internal vocabularies (VerticalNet, Cisco)

  8. Status / Directions • Chimaera provides merging and diagnostic support for ontologies in many formats • It can be used offline in batch mode or interactively as an integrated browser/editor • It is becoming extensible with rule language • It improves merging performance over existing tools • It has been used by people of various training backgrounds in government and commercial applications and is available for use. • Will be able to explain its suggestions • Support collaborative development • Handling deeper representations • http://www.ksl.Stanford.EDU/software/chimaera/ -movie, tutorial, papers(KR2000, AAAI2000, ICCS 2000), link to live system, etc.

  9. Extras

  10. Motivation: Ontology Integration Trends • Integrated in most search applications (Yahoo, Lycos, Xift, …) • Core component of E-Commerce applications (Amazon, eBay, Virtual Vineyards, REI, VerticalNet, CommerceOne, etc.) • Integrated in configuration applications (Dell, PROSE, etc.)

  11. Motivation: Ontology Evolution • Controlled vocabularies abound (SIC-codes, UN/SPSC, RosettaNet, OpenDirectory,…) • Distributed ownership/maintenance • Larger scale (Open Directory >23.5K editors, ~250K categories, 1.65M sites) • Becoming more complicated - Moving to classes and slots (and value restrictions, enumerated sets, cardinality)

  12. The Need For KB Merging • Large-scale knowledge repositories will contain KBs produced by multiple authors in multiple settings • KBs for applications will be built by assembling and extending multiple modular KBs from repositories • KBs developed by multiple authors will frequently • Express overlapping knowledge in a common domain • Use differing representations and vocabularies • For such KBs to be used together as building blocks - Their representational differences must be reconciled

  13. The KB Merging Task • Combine KBs that: • Were developed independently (by multiple authors) • Express overlapping knowledge in a common domain • Use differing representations and vocabularies • Produce merged KB with • Non-redundant • Coherent • Unified vocabulary, content, and representation

  14. Merging Tools • Merging can be arbitrarily difficult • KBs can differ in basic representational design • May require extensive negotiation among authors • Tools can significantly accelerate major steps • KB merging using conventional editing tools is • Difficult Labor intensive Error prone • Hypothesis: tools specifically designed to support KB merging can significantly • Speed up the merging process • Make broader user set productive • Improve the quality of the resulting KB

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