Learning Onto-Relational Rules with Inductive Logic Programming
This addresses the demanding knowledge engineering task for Semantic Web developers, but it is incremental as it adapts existing ILP methods to a new domain.
The paper tackles the problem of automating rule authoring for Semantic Web ontologies by adapting Inductive Logic Programming (ILP) to learn onto-relational rules, specifically demonstrating a solution for learning rule-based definitions of Description Logic concepts and roles.
Rules complement and extend ontologies on the Semantic Web. We refer to these rules as onto-relational since they combine DL-based ontology languages and Knowledge Representation formalisms supporting the relational data model within the tradition of Logic Programming and Deductive Databases. Rule authoring is a very demanding Knowledge Engineering task which can be automated though partially by applying Machine Learning algorithms. In this chapter we show how Inductive Logic Programming (ILP), born at the intersection of Machine Learning and Logic Programming and considered as a major approach to Relational Learning, can be adapted to Onto-Relational Learning. For the sake of illustration, we provide details of a specific Onto-Relational Learning solution to the problem of learning rule-based definitions of DL concepts and roles with ILP.