Learning from good and bad data
Learning from good and bad data
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
The substitutional framework for sorted deduction: fundamental results on hybrid reasoning
Artificial Intelligence - Special issue on knowledge representation
Anti-unification in constraint logics: foundations and applications to learnability in first-order logic, to speed-up learning, and to deduction
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Building theories into instantiation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Relational Data Mining Applied to Virtual Engineering of Product Designs
Inductive Logic Programming
ECML '07 Proceedings of the 18th European conference on Machine Learning
Principles of inductive reasoning on the semantic web: a framework for learning in AL-log
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
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Since its inception, the field of inductive logic programming has been centrally concerned with the use of background knowledge in induction. Yet, surprisingly, no serious attempts have been made to account for background knowledge in refinement operators for clauses, even though such operators are one of the most important, prominent and widely-used devices in the field. This paper shows how a sort theory, which encodes taxonomic knowledge, can be built into a downward, subsumption-based refinement operator for clauses.