A study of relevance for learning in deductive databases

Abstract

This paper is a study of the problem of relevance in inductive concept learning. It gives definitions of irrelevant literals and irrelevant examples and presents efficient algorithms that enable their elimination. The proposed approach is directly applicable in propositional learning and in relation learning tasks that can be solved using a LINUS transformation approach. A simple inductive logic programming (ILP) problem is used to illustrate the approach to irrelevant literal and example elimination. Results of utility studies show the usefulness of literal reduction applied in LINUS and in the search of refinement graphs.