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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations