Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood

Abstract

In inductive logic programming, µ-subsumption is a widely used coveragetest. Unfortunately, testing µ-subsumption is NP-complete, which represents a crucial efficiency bottleneck for many relational learners. In this paper, we present aprobabilistic estimator of clause coverage,based on a randomized restarted search strategy. Under a distribution assumption,our algorithm can estimate clause coverage without having to decide subsumption for all examples. We implement this algorithm in program ReCovEr. On generated graph data and real-world datasets, we show that ReCovEr provides reasonably accurate estimates while achieving dramatic runtimes improvements compared to a state-of-the-art algorithm.