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Structure learning of probabilistic logic programs by searching the clause space

Published online by Cambridge University Press:  15 January 2014

ELENA BELLODI  and
FABRIZIO RIGUZZI
ELENA BELLODI
Affiliation:
Dipartimento di Ingegneria – University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mail: elena.bellodi@unife.it)
FABRIZIO RIGUZZI
Affiliation:
Dipartimento di Matematica e Informatica – University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy (e-mail: fabrizio.riguzzi@unife.it)
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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 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.

Keywords

probabilistic inductive logic programmingstatistical relational learningstructure learningdistribution semanticslogic programs with annotated disjunctionCP-logic
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 15 , Issue 2: Probability, Logic and Learning , March 2015 , pp. 169 - 212
Copyright
Copyright © Cambridge University Press 2014 

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