Representing biases for inductive logic programming
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Biases and their effects in inductive logic programming
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Induction in first order logic from noisy training examples and fixed example set sizes
Induction in first order logic from noisy training examples and fixed example set sizes
An algorithm for incremental mode induction
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Knowledge Bases Over Algebraic Models: Some Notes About Informational Equivalence
International Journal of Knowledge Management
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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.