Structure Learning of Probabilistic Logic Programs by Searching the Clause Space

TL;DR

This paper introduces SLIPCOVER, an algorithm for learning the structure of probabilistic logic programs by searching clause space, outperforming existing methods on real datasets.

Contribution

SLIPCOVER is a novel algorithm that combines beam search and greedy search with EM to learn probabilistic logic program structures effectively.

Findings

SLIPCOVER outperforms existing algorithms on several datasets.

It achieves higher precision-recall and ROC curve areas.

The method effectively searches clause space for better probabilistic logic models.

Abstract

Learning probabilistic logic programming languages is receiving an increasing attention and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both the structure and the parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for "Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space". It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories, using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and ROC curves in most cases.