Creating, Maintaining, and Integrating Understandable Knowledge Bases

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

7. 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

8. 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, ...

11. 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)

12. 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.

13. Extras

14. 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.)

15. 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)

16. 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

17. 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

19. 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