  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.
