Compressing probabilistic Prolog programs

被引：29

作者：

De Raedt, L. ^{[1
]}

Kersting, K. ^{[2
]}

Kimmig, A. ^{[1
]}

Revoredo, K. ^{[2
]}

Toivonen, H. ^{[3
]}

机构：

[1] Katholieke Univ Leuven, Dept Computerwetenschappen, B-3001 Heverlee, Belgium

[2] Univ Freiburg, Inst Informat, D-79100 Freiburg, Germany

[3] Univ Helsinki, Dept Comp Sci, FIN-00014 Helsinki, Finland

来源：

MACHINE LEARNING | 2008年 / 70卷 / 2-3期

关键词：

probabilistic logic; inductive logic programming; theory revision; compression; network mining; biological applications; statistical relational learning;

D O I：

10.1007/s10994-007-5030-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

ProbLog is a recently introduced probabilistic extension of Prolog (De Raedt, et al. in Proceedings of the 20th international joint conference on artificial intelligence, pp. 2468-2473, 2007). A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is then defined by the success probability of a query in a randomly sampled program. This paper introduces the theory compression task for ProbLog, which consists of selecting that subset of clauses of a given ProbLog program that maximizes the likelihood w.r.t. a set of positive and negative examples. Experiments in the context of discovering links in real biological networks demonstrate the practical applicability of the approach.

引用

页码：151 / 168

页数：18

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