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Creating , Maintaining , and Integrating Understandable Knowledge Bases Richard Fikes Deborah McGuinness Sheila McIlrai

Creating , Maintaining , and Integrating Understandable Knowledge Bases Richard Fikes Deborah McGuinness Sheila 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 Richard Fikes Deborah McGuinness Sheila McIlrai

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  1. Creating, Maintaining,and Integrating Understandable Knowledge Bases Richard Fikes Deborah McGuinness Sheila 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

  2. 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, …

  3. 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

  4. 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

  5. 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

  6. 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)

  7. 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

  8. 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?”

  9. 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)

  10. 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

  11. 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]

  12. 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?

  13. 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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