kFOIL: Learning Simple Relational Kernels

A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification and regression tasks can be naturally handled. Experiments in applying kFOIL to well-known benchmarks in chemoinformatics show the promise of the approach. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.