Structure Learning of Probabilistic Logic Programs by Searching the Clause Space
This addresses the challenge of structure learning in probabilistic logic programming for AI researchers, offering incremental improvements over prior systems.
The paper tackles the problem of learning the structure of probabilistic logic programs by introducing SLIPCOVER, an algorithm that searches over clause spaces using beam and greedy search guided by log likelihood. It shows that SLIPCOVER achieves higher areas under precision-recall and ROC curves compared to existing methods on five real-world datasets.
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.