Preprocessing in Inductive Logic Programming

TL;DR

This paper introduces bottom preprocessing, a new method for generating initial constraints in inductive logic programming, which improves learning efficiency especially with large background knowledge.

Contribution

It presents bottom preprocessing and its implementation in $ot$-Popper, demonstrating significant reductions in ILP learning times on complex problems.

Findings

Reduces ILP learning times on hard problems.

Especially effective with large background knowledge.

Experimental results show notable efficiency gains.

Abstract

Inductive logic programming is a type of machine learning in which logic programs are learned from examples. This learning typically occurs relative to some background knowledge provided as a logic program. This dissertation introduces bottom preprocessing, a method for generating initial constraints on the programs an ILP system must consider. Bottom preprocessing applies ideas from inverse entailment to modern ILP systems. Inverse entailment is an influential early ILP approach introduced with Progol. This dissertation also presents $\bot$-Popper, an implementation of bottom preprocessing for the modern ILP system Popper. It is shown experimentally that bottom preprocessing can reduce learning times of ILP systems on hard problems. This reduction can be especially significant when the amount of background knowledge in the problem is large.