Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

TL;DR

This paper introduces a framework that uses Inductive Logic Programming to automate rule induction on ontologies, enhancing the Semantic Web's logical layer with a method applicable to description and prediction tasks.

Contribution

It presents a novel general framework combining ILP and $ ext{AL}$-log for rule induction on ontologies, applicable to ontology refinement and Semantic Web reasoning.

Findings

Framework effectively automates rule acquisition for ontologies.

Applicable to both description and prediction tasks.

Enhances ontology refinement processes.

Abstract

Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as $\mathcal{AL}$-log that integrates the description logic $\mathcal{ALC}$ and the function-free Horn clausal language \textsc{Datalog}. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of $\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement. Keywords: Inductive Logic Programming, Hybrid Knowledge Representation and Reasoning Systems, Ontologies, Semantic Web. Note: To appear in Theory and Practice of Logic Programming (TPLP)