DL-Lite Bayesian networks: A tractable probabilistic graphical model

Denis D. Mauá, Fabio G. Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

© Springer International Publishing Switzerland 2015. The construction of probabilistic models that can represent large systems requires the ability to describe repetitive and hierarchical structures. To do so, one can resort to constructs from description logics. In this paper we present a class of relational Bayesian networks based on the popular description logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effort. We show this by reductions to edge covering problems, and derive a result of independent interest; namely, that counting edge covers in a particular class of graphs requires polynomial effort.
Original languageAmerican English
Title of host publicationDL-Lite Bayesian networks: A tractable probabilistic graphical model
Pages50-64
Number of pages15
ISBN (Electronic)9783319235394
DOIs
StatePublished - 1 Jan 2015
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9310
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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  • Cite this

    Mauá, D. D., & Cozman, F. G. (2015). DL-Lite Bayesian networks: A tractable probabilistic graphical model. In DL-Lite Bayesian networks: A tractable probabilistic graphical model (pp. 50-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9310). https://doi.org/10.1007/978-3-319-23540-0_4