On the implementation of the probabilistic logic programming language ProbLog

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 networ...

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Veröffentlicht in:Theory and practice of logic programming Jg. 11; H. 2-3; S. 235 - 262
Hauptverfasser: KIMMIG, ANGELIKA, DEMOEN, BART, DE RAEDT, LUC, COSTA, VÍTOR SANTOS, ROCHA, RICARDO
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 01.03.2011
Schlagworte:
Biological
Learning
Logic
Logic programming
Networks
Probabilistic methods
Probability theory
Prolog (programming language)
Queries
Regular Papers
Exact and approximative inference
Probabilistic logic programming
Implementation
ISSN:1471-0684, 1475-3081
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068410000566