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.
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.
- Converting Semantic Meta-knowledge into Inductive Bias
- Book Title
- Inductive Logic Programming
- Book Subtitle
- 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005. Proceedings
- pp 38-50
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Industry Sectors
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- Editor Affiliations
- 18. Institut für Informatik I12, Technische Universität München
- 19. Department of Computer Science, University of Waikato
- Author Affiliations
- 20. Cycorp, Inc., 3721 Executive Center Drive, Suite 100, Austin, TX, 78739, USA
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