Pairwise saturations in inductive logic programming

Abstract

One of the main issues when using inductive logic programming (ILP) in practice remain the long running times that are needed by ILP systems to induce the hypothesis. We explore the possibility of reducing the induction running times of systems that use asymmetric relative minimal generalisation (ARMG) by analysing the bottom clauses of examples that serve as inputs into the generalisation operator. Using the fact that the ARMG covers all of the examples and that it is a subset of the variabilization of one of the examples, we identify literals that cannot appear in the ARMG and remove them prior to computing the generalisation. We apply this procedure to the ProGolem ILP system and test its performance on several real world data sets. The experimental results show an average speedup of $$36\,\%$$36% compared to the base ProGolem system and $$12\,\%$$12% compared to ProGolem extended with caching, both without a decrease in the accuracy of the produced hypotheses. We also observe that the gain from using the proposed method varies greatly, depending on the structure of the data set.