Scaling Structure Learning of Probabilistic Logic Programs by MapReduce

被引：1

作者：

Riguzzi, Fabrizio ^{[1
]}

Bellodi, Elena ^{[2
]}

Zese, Riccardo ^{[2
]}

Cota, Giuseppe ^{[2
]}

Lamma, Evelina ^{[2
]}

机构：

[1] Univ Ferrara, Dipartimento Matemat & Informat, Via Saragat 1, I-44122 Ferrara, Italy

[2] Univ Ferrara, Dipartimento Ingn, Via Saragat 1, I-44122 Ferrara, Italy

来源：

ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年 / 285卷

关键词：

D O I：

10.3233/978-1-61499-672-9-1602

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1602 / 1603

页数：2

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