PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Compressing probabilistic Prolog programs
Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo and Hannu Toivonen
Machine Learning Volume 70, Number 2-3, pp. 151-168, 2008. ISSN 0885-6125

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5922
Deposited By:Hannu Toivonen
Deposited On:08 March 2010