Structure learning of probabilistic logic programs by searching the clause space

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 structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algo...

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Bibliographic Details
Published in:Theory and practice of logic programming 2015-03, Vol.15 (2), p.169-212
Main Authors: BELLODI, ELENA, RIGUZZI, FABRIZIO
Format: Article
Language:English
Subjects:
Algorithms
Learning
Logic programming
Maximization
Prisms
Probabilistic methods
Probability theory
Receiving
Regular Papers
Searching
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Summary: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 structure and 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 receiver operating characteristic curves in most cases.
ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068413000689