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Creating , Maintaining , and Integrating Understandable Knowledge Bases PowerPoint PPT Presentation


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Creating , Maintaining , and Integrating Understandable Knowledge Bases Richard FikesDeborah McGuinnessSheila McIlraith Jessica Jenkins Steve Wilder Kengo Ishii Gleb Frank Yulin Li Honglei Zeng Knowledge Systems Laboratory Stanford University

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Creating , Maintaining , and Integrating Understandable Knowledge Bases

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Creating,

Maintaining,and Integrating

Understandable

Knowledge Bases

Richard FikesDeborah McGuinnessSheila McIlraith

Jessica Jenkins Steve Wilder Kengo Ishii

Gleb Frank Yulin Li Honglei Zeng

Knowledge Systems Laboratory

Stanford University

www.ksl.stanford.edu

1/24/01


Forming Understandable Knowledge

  • Knowledge formation requiresknowledge evolution

    • KBs require multiple developmental steps to become useful

    • KSL is building tools to support KB evolution

      • KB diagnostics

        • Bugs, missing knowledge, heuristic warnings, architectural advice

      • KB explanation

        • Customized to individual users and tasks

      • KB merging

        • Consistency checking using a hybrid reasoner (JTP)

      • KB modularization

        • To produce reusable KB building blocks

  • Knowledge formation requires expressive KR languages

    • KSL is extending current representation formalisms

      • Defaults, KB partitioning, perspectives, …


Chimaera

  • A Knowledge Evolution Environment

    • Tools for KB diagnosis and merging

  • Available as a Web service

    • www-ksl-svc.stanford.edu

    • Usable from a Web browser

    • Online user manual, tutorial, and demonstration movie

  • Performs KB diagnostics in batch mode

    • Uploads and analyzes user’s KB

    • Accepts KBs in MELD, KIF, OKBC, RDF, XML, DAML, …

    • Provides results as HTML pages linked to frames and axioms

    • Provides user selectable set of diagnostic tests

  • Analyzes both the structure and content of a KB

    • Uses reasoners to analyze content

    • Currently runs 28 diagnostic tests


Recent Publications and Presentations

  • AAAI 2000

    • Deborah McGuinness, Richard Fikes, James Rice, and Steve Wilder; “The Chimaera Ontology Environment”; Intelligent Systems Track, Seventeenth National Conference on Artificial Intelligence; Austin, Texas; July 30 - August 3, 2000.

  • ICCS 2000

    • Deborah McGuinness; “Conceptual Modeling for Distributed Ontology Environments”; Eighth International Conference on Conceptual Structures: Logical, Linguistic, and Computational Issues; Darmstadt, Germany; August 14-18, 2000.

  • Invited Talks

    • Deborah McGuinness; “Ontology Environments”;

      • Autumn School for Cognitive Science – Freiburg, Germany, Sept., 2000

      • Free University of Amsterdam (Vrij) – Sept. 2000

      • Sun Microsystems – Palo Alto, CA – Dec. 2000

      • National Center for Atmospheric Research – Boulder, CO, coming Feb 2001


Classification of Diagnostic Results

  • Errors

    • Logical inconsistencies

      E.g., contradictory type constraints

    • Content structure errors

      E.g., terms used but not defined

  • Anomalies

    • Missing information

      E.g., type constraints

    • Redundancies

      E.g., redundant superclass and type links

    • Extraneous structure or content

      E.g., terms defined but not used

  • Summaries

    E.g., counts of term references

  • Suggestions

    E.g., use consistent naming conventions


Diagnose Both Frames and Axioms

  • Examples of frame-oriented diagnostics

    • Local constraint contradicts inherited constraint (error)

    • Object an instance of disjoint classes (error)

    • Cyclic subclass links (anomaly)

    • Class with a single subclass (anomaly)

    • Object an instance of a non-leaf class (anomaly)

    • Class contains no local information (anomaly)

    • Include disjointness statements about class siblings (suggestion)

  • Examples of axiom-oriented diagnostics

    • Quantified variable not in the body of the axiom (anomaly)

    • Variable in implication antecedent but not in consequent (anomaly)

    • Illegal number of arguments for implication or negation (error)

    • Conjunction or disjunction with only one argument (anomaly)

    • Suggest breaking up exceptionally long axioms (suggestion)


Next Steps: Repair Dialogues

  • Provide interactive follow-up to diagnostics

    • Identify KB content on which diagnosis result is based

    • Suggest repairs or repair strategies

    • Guide user through repair procedure

  • Examples

    • Class is a direct subclass of “THING”

      • Provide direct subclasses of THING as candidate superclasses

      • Step down through the class hierarchy

    • Class has redundant superclass links

      • Present the redundant links

      • Suggest removal of link(s) to most general classes

    • Type, cardinality, or bounds conflict

      • Present conflicting constraints

      • Suggest changing local conflicting constraint(s)

    • Missing information

      • Initiate acquisition dialogues for missing information


Next Steps: Acquisition Dialogues

  • Chimaera notes missing information about “parent” of “Person”

  • User requests that Chimaera initiate an acquisition dialogue

  • Chimaera responds by asking questions:

    • “How many parents must a person have?”

      • n

      • At least n

      • At most n

      • Any number

      • Don’t know

    • “What kind of an object is a parent of a person?”

      • The parent is a …

  • Assume the SME responds:

    • “The parent is a person and a cat”

  • Chimaera might respond:

    • “A person cannot also be a cat. Is a parent of a person always a person?”


Next Steps: “Background” Analysis

  • Reasoning tests that may take substantial time

    • Performed in background

    • Results incrementally posted on Web page

    • Result summaries sent to user via e-mail when ready

  • Example tests

    • Redundant axioms that are inferred by the KB (anomaly)

    • Inconsistent axioms whose negations are inferred by the KB (error)

    • Determine which relations in KB are primitive and non-primitive (summary)

      • Show relations on which each non-primitive relation depend

    • Determine classes that are disjoint (suggest adding results to KB)

    • Derive subclass and instance of links (suggest adding links to KB)

      I.e., classification and recognition

    • Suggest reordering of an implication’s antecedents based on number of inferable instances of each antecedent (suggestion)


Contributions To SRI Team

  • Defaults

    • Delivered design document, June 2000

    • Providing design support for defaults in the Summer 2001 KB

  • Partition-Based Logical Reasoning

    • Techniques for answering queries from large KBs

    • Working jointly with Eyal Amir in John McCarthy’s group

  • Knowledge Base Diagnostics

    • SRI has provided sample RKF KBs

    • KSL has diagnosed the sample KBs and obtained feedback from SRI

    • KSL to provide a KB diagnosis service on an ongoing basis

      • SRI team to provide evaluation feedback on the diagnosis service

    • KSL to develop strategies for KB repair dialogues

    • KSL to develop strategies for incremental diagnosis


KB Diagnosis Component Evaluation

  • Evaluation methodology

    • Obtain structured feedback from KB developers

      • “Check Box” feedback on individual diagnosis results

      • Follow-up questions on a sampling of diagnosis results

      • Summary assessments of overall value of diagnosis

    • Record and analyze repair dialogue use

  • Sample “Check Box” questions for KB developer

    • Does this diagnostic provide information you did not know? [yes no]

    • Does this diagnostic provide information you need to know? [1 2 3 4 5]

    • Are you going to change the KB in response to this diagnostic? [yes perhaps no]

    • How difficult would it be to obtain this information in some other way? [1 2 3 4 5]

    • Is the diagnostic understandable? [yes mostly marginally no]

    • Was running these diagnostics worth the time and effort? [1 2 3 4 5]


KB Diagnosis Component Evaluation

  • Sample follow-up questions

    • Check box question:

      Is the diagnostic understandable? [yes mostly marginally no]

    • Follow-up question:

      What would have made the diagnostic more understandable?

    • Check box question:

      Does this diagnostic provide information you need to know? [1 2 3 4 5]

    • Follow-up question:

      What property or capability of the KB did this diagnostic enable you to improve?

  • Sample summary assessment questions

    • What diagnostic information about this KB would you like to have that is not provided by Chimaera?

    • What would make running these diagnostics more worthwhile?


Summary

  • KSL is building the Chimaera tool suite to support KB evolution

  • Our current focus is on diagnosing KBs

    • Providing a KB diagnosis Web service

    • Finding errors and anomalies in both structure and content

    • Providing advice to KB authors

    • Using reasoners to provide sophisticated diagnostics

    • Developing KB repair and acquisition dialogues

  • We are providing support to the SRI team in multiple ways

    • Defaults

    • Partition-Based Logical Reasoning

    • KB Diagnostics

  • We will do a component evaluation experiment

    • To evaluate Chimaera’s KB diagnostics

    • Based on structured feedback from KB developers and repair dialogue use


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