creating, maintaining, and integrating understandable knowledge bases
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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 [email protected] Joint work with - PowerPoint PPT PresentationTRANSCRIPT
Creating, Maintaining, and Integrating Creating, Maintaining, and Integrating Understandable Knowledge BasesUnderstandable Knowledge Bases
Deborah L. McGuinness Deborah L. McGuinness
Associate Director and Senior Research ScientistAssociate Director and Senior Research ScientistKnowledge Systems LaboratoryKnowledge Systems Laboratory
Stanford UniversityStanford UniversityStanford, CA 94305Stanford, CA 94305
650-723-9770650-723-9770 [email protected]
Joint work with Joint work with Steve Wilder Jessica Jenkins Steve Wilder Jessica Jenkins Honglei Zeng
Zhou Qing Gleb FrankZhou Qing Gleb Frank
Research Directions (Formation and Evolution)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
Chimaera – A Merging and Chimaera – A Merging and Diagnostic Ontology EnvironmentDiagnostic 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
Our KB Analysis TaskOur 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
IntegrationIntegration 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
How KB Merging Tools Can HelpHow 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, ...
Chimaera I UsageChimaera 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)
Status / DirectionsStatus / Directions• Chimaera provides merging and diagnostic support for ontologies in Chimaera provides merging and diagnostic support for ontologies in
many formatsmany formats• It can be used offline in batch mode or interactively as an integrated It can be used offline in batch mode or interactively as an integrated
browser/editorbrowser/editor• It is becoming extensible with rule languageIt is becoming extensible with rule language• It improves merging performance over existing toolsIt improves merging performance over existing tools• It has been used by people of various training backgrounds in It has been used by people of various training backgrounds in
government and commercial applications and is available for use.government and commercial applications and is available for use.• Will be able to explain its suggestionsWill be able to explain its suggestions• Support collaborative developmentSupport collaborative development• Handling deeper representationsHandling deeper representations• http://www.ksl.Stanford.EDU/software/chimaera/ -movie, tutorial,
papers(KR2000, AAAI2000, ICCS 2000), link to live system, etc.
Motivation: Ontology Integration TrendsMotivation: 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.)
Motivation: Ontology EvolutionMotivation: 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)
The Need For KB MergingThe 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
The KB Merging TaskThe 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
Merging ToolsMerging 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
Experiment 3: Chimæra vs. Ontolingua editor
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