Compressing probabilistic Prolog programs |
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Authors: | L. De Raedt K. Kersting A. Kimmig K. Revoredo H. Toivonen |
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Affiliation: | 2. Departement Computerwetenschappen, K.U. Leuven, Celestijnenlaan 200A, bus 2402, 3001, Heverlee, Belgium 1. Institut für Informatik, Albert-Ludwigs-Universit?t, Georges-K?hler-Allee, Geb?ude 079, 79110, Freiburg im Breisgau, Germany 3. Department of Computer Science, University of Helsinki, P.O. Box 68, 00014, Helsinki, Finland
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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. Editors: Stephen Muggleton, Ramon Otero, Simon Colton. |
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Keywords: | Probabilistic logic Inductive logic programming Theory revision Compression Network mining Biological applications Statistical relational learning |
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