Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning

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 θ-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.