Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs

TL;DR

This paper introduces query packs to structure and execute similar queries efficiently in inductive logic programming, significantly improving system performance through a new execution mechanism supported by empirical validation.

Contribution

The paper proposes query packs as a novel method to enhance ILP efficiency and details an execution mechanism that yields substantial performance improvements.

Findings

Query packs reduce ILP execution time.

Empirical results show significant efficiency gains.

Support for query packs improves existing ILP systems.

Abstract

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.