Journal
THEORY AND PRACTICE OF LOGIC PROGRAMMING
Volume 11, Issue -, Pages 235-262Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S1471068410000566
Keywords
Probabilistic logic programming; Exact and approximative inference; Implementation
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The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.
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