Selective propositionalization for relational learning

Abstract

A number of Inductive Logic Programming (ILP) systems have addressed the problem of learning First Order Logic (FOL) discriminant definitions by first reformulating the problem expressed in a FOL framework into a attribute-value problem and then applying efficient algebraic learning techniques. The complexity of such propositionalization methods is now in the size of the reformulated problem which can be exponential. We propose a method that selectively propositionalizes the FOL training set by interleaving boolean reformulation and algebraic resolution. It avoids, as much as possible, the generation of redundant boolean examples, and still ensures that explicit correct and complete definitions are learned.