ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
ILP Through Propositionalization and Stochastic k-Term DNF Learning
Inductive Logic Programming
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
The limits on combining recursive horn rules with description logics
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Relational subgroup discovery for descriptive analysis of microarray data
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
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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Knowledge representations using semantic web technologies often provide information which translates to explicit term and predicate taxonomies in relational learning. We show how to speed up the propositionalization by orders of magnitude, by exploiting such taxonomies through a novel refinement operator used in the construction of conjunctive relational features. Moreover, we accelerate the subsequent propositional search using feature generality taxonomy, determined from the initial term and predicate taxonomies and 茂戮驴-subsumption between features. This enables the propositional rule learner to prevent the exploration of conjunctions containing a feature together with any of its subsumees and to specialize a rule by replacing a feature by its subsumee. We investigate our approach with a deterministic top-down propositional rule learner, and propositional rule learner based on stochastic local search.