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 SLIPCOVER 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 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.
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce / Riguzzi, Fabrizio; Bellodi, Elena; Zese, Riccardo; Cota, Giuseppe; Lamma, Evelina. - STAMPA. - 285:(2016), pp. 1602-1603. (Intervento presentato al convegno 22nd European Conference on Artificial Intelligence tenutosi a The Hague nel August 29-September 2, 2016) [10.3233/978-1-61499-672-9-1602].
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce
COTA, Giuseppe;
2016-01-01
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 SLIPCOVER 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 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
