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Scaling Structure Learning of Probabilistic Logic Programs by MapReduce
Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Giuseppe Cota, Evelina Lamma
1602 - 1603
10.3233/978-1-61499-672-9-1602
Frontiers in Artificial Intelligence and Applications
Volume 285: ECAI 2016
Abstract

Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instancethe system SLIPCOVER learns high quality theories in a variety of domains. HoweverSLIPCOVER is computationally expensivewith a running time of the order of hours. In order to apply SLIPCOVER to Big Data, we present SEMPRE, for “Structure lEarning by MaPREduce”, that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface.

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