Author:

Keywords:

Probabilistic logic, Inductive logic programming, Theory revision, Compression, Network mining, Biological applications, Statistical relational learning, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, probabilistic logic, inductive logic programming, theory revision, compression, network mining, biological applications, statistical relational learning, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 4611 Machine learning

Abstract:

ProbLog is a recently introduced probabilistic extension of Prolog [De Raedt et al. IJCAI 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.