Inductive Logic Programming

Volume 3625 of the series Lecture Notes in Computer Science pp 38-50

Converting Semantic Meta-knowledge into Inductive Bias

  • John CabralAffiliated withCycorp, Inc.
  • , Robert C. KahlertAffiliated withCycorp, Inc.
  • , Cynthia MatuszekAffiliated withCycorp, Inc.
  • , Michael WitbrockAffiliated withCycorp, Inc.
  • , Brett SummersAffiliated withCycorp, Inc.

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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.