DL-Lite Bayesian networks: A tractable probabilistic graphical model

Denis D. Mauá; Fabio G. Cozman

doi:10.1007/978-3-319-23540-0_4

DL-Lite Bayesian networks: A tractable probabilistic graphical model

Denis D. Mauá, Fabio G. Cozman

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	American English
Title of host publication	DL-Lite Bayesian networks: A tractable probabilistic graphical model
Pages	50-64
Number of pages	15
ISBN (Electronic)	9783319235394
DOIs	https://doi.org/10.1007/978-3-319-23540-0_4
State	Published - 1 Jan 2015
Externally published	Yes
Event	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duration: 1 Jan 2018 → …

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9310
ISSN (Print)	0302-9743

Conference

Conference	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period	1/01/18 → …

Access to Document

10.1007/978-3-319-23540-0_4

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

@inproceedings{07d34c48839f4208b325af1a7a20dd2d,

title = "DL-Lite Bayesian networks: A tractable probabilistic graphical model",

abstract = "{\textcopyright} 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.",

author = "Mau{\'a}, {Denis D.} and Cozman, {Fabio G.}",

year = "2015",

month = jan,

day = "1",

doi = "10.1007/978-3-319-23540-0_4",

language = "American English",

isbn = "9783319235394",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "50--64",

booktitle = "DL-Lite Bayesian networks: A tractable probabilistic graphical model",

note = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Conference date: 01-01-2018",

}

Mauá, DD & Cozman, FG 2015, DL-Lite Bayesian networks: A tractable probabilistic graphical model. in DL-Lite Bayesian networks: A tractable probabilistic graphical model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9310, pp. 50-64, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/18. https://doi.org/10.1007/978-3-319-23540-0_4

DL-Lite Bayesian networks: A tractable probabilistic graphical model. / Mauá, Denis D.; Cozman, Fabio G.

DL-Lite Bayesian networks: A tractable probabilistic graphical model. 2015. p. 50-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9310).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - DL-Lite Bayesian networks: A tractable probabilistic graphical model

AU - Mauá, Denis D.

AU - Cozman, Fabio G.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - © 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.

AB - © 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.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951873460&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84951873460&origin=inward

U2 - 10.1007/978-3-319-23540-0_4

DO - 10.1007/978-3-319-23540-0_4

M3 - Conference contribution

SN - 9783319235394

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 50

EP - 64

BT - DL-Lite Bayesian networks: A tractable probabilistic graphical model

T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Y2 - 1 January 2018

ER -

DL-Lite Bayesian networks: A tractable probabilistic graphical model

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this