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 instance, the system SLIP-COVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours. In order to apply SLIP-COVER 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.

Scaling Structure Learning of Probabilistic Logic Programs by MapReduce

F. Riguzzi
;
E. Bellodi;R. Zese;E. Lamma
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 instance, the system SLIP-COVER learns high quality theories in a variety of domains. However, SLIPCOVER is computationally expensive, with a running time of the order of hours. In order to apply SLIP-COVER 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.
2016
9781614996712
Probabilistic Logic Programming, Parameter Learning, Structure Learning, MapReduce
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2350955
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