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An ILP Perspective on the Semantic Web

Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The system AL-log, originally conceived for hybrid Knowledge Representation and Reasoning (KR&R), has been very recently mentioned as the blueprint for well-founded Semantic Web rule mark-up languages. It integrates the description logic ALC and the function-free Horn clausal language Datalog. In this paper we provide a framework for learning Semantic Web rules which adopts Inductive Logic Programming (ILP) as methodological apparatus and AL-log as KR&R setting. In this framework inductive hypotheses are represented as constrained Datalog clauses, organized according to the B-subsumption relation, and evaluated against observations by means of coverage relations. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we concentrate on an instantiation of the framework which supports description.