Converting Semantic Meta-knowledge into Inductive Bias

  • John Cabral
  • Robert C. Kahlert
  • Cynthia Matuszek
  • Michael Witbrock
  • Brett Summers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3625)

Abstract

The Cyc KB has a rich pre-existing ontology for representing common sense knowledge. To clarify and enforce its terms’ semantics and to improve inferential efficiency, the Cyc ontology contains substantial meta-level knowledge that provides definitional information about its terms, such as a type hierarchy. This paper introduces a method for converting that meta-knowledge into biases for ILP systems. The process has three stages. First, a “focal position” for the target predicate is selected, based on the induction goal. Second, the system determines type compatibility or conflicts among predicate argument positions, and creates a compact, efficient representation that allows for syntactic processing. Finally, mode declarations are generated, taking advantage of information generated during the first and second phases.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • John Cabral
    • 1
  • Robert C. Kahlert
    • 1
  • Cynthia Matuszek
    • 1
  • Michael Witbrock
    • 1
  • Brett Summers
    • 1
  1. 1.Cycorp, Inc.AustinUSA

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