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 dlm@ksl.stanford.edu

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.

ExtrasExtras

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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Ontolingua Editor