On the implementation of the probabilistic logic programming language ProbLog

Detalhes bibliográficos
Autor(a) principal: Vítor Santos Costa
Data de Publicação: 2011
Outros Autores: Angelika Kimmig, Bart Demoen, Luc De Raedt, Ricardo Rocha
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/3472
http://dx.doi.org/10.1017/S1471068410000566
Resumo: 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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spelling On the implementation of the probabilistic logic programming language ProbLogThe 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.2017-11-17T13:50:58Z2011-01-01T00:00:00Z2011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/3472http://dx.doi.org/10.1017/S1471068410000566engVítor Santos CostaAngelika KimmigBart DemoenLuc De RaedtRicardo Rochainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:19:41Zoai:repositorio.inesctec.pt:123456789/3472Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:05.399996Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv On the implementation of the probabilistic logic programming language ProbLog
title On the implementation of the probabilistic logic programming language ProbLog
spellingShingle On the implementation of the probabilistic logic programming language ProbLog
Vítor Santos Costa
title_short On the implementation of the probabilistic logic programming language ProbLog
title_full On the implementation of the probabilistic logic programming language ProbLog
title_fullStr On the implementation of the probabilistic logic programming language ProbLog
title_full_unstemmed On the implementation of the probabilistic logic programming language ProbLog
title_sort On the implementation of the probabilistic logic programming language ProbLog
author Vítor Santos Costa
author_facet Vítor Santos Costa
Angelika Kimmig
Bart Demoen
Luc De Raedt
Ricardo Rocha
author_role author
author2 Angelika Kimmig
Bart Demoen
Luc De Raedt
Ricardo Rocha
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Vítor Santos Costa
Angelika Kimmig
Bart Demoen
Luc De Raedt
Ricardo Rocha
description 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.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01T00:00:00Z
2011
2017-11-17T13:50:58Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/3472
http://dx.doi.org/10.1017/S1471068410000566
url http://repositorio.inesctec.pt/handle/123456789/3472
http://dx.doi.org/10.1017/S1471068410000566
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