Complexity analysis and variational inference for interpretation-based probabilistic description logics

Fabio Gagliardi Cozman; Rodrigo Bellizia Polastro

Complexity analysis and variational inference for interpretation-based probabilistic description logics

Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

20 Citas (Scopus)

Resumen

This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.

Idioma original	Inglés estadounidense
Páginas	117-125
Número de páginas	9
Estado	Publicada - 1 dic 2009
Publicado de forma externa	Sí
Evento	Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Duración: 1 dic 2009 → …

Conferencia

Conferencia	Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Período	1/12/09 → …

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title = "Complexity analysis and variational inference for interpretation-based probabilistic description logics",

abstract = "This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.",

author = "Cozman, {Fabio Gagliardi} and Polastro, {Rodrigo Bellizia}",

year = "2009",

month = dec,

day = "1",

language = "American English",

pages = "117--125",

note = "Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 ; Conference date: 01-12-2009",

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Complexity analysis and variational inference for interpretation-based probabilistic description logics. / Cozman, Fabio Gagliardi; Polastro, Rodrigo Bellizia.

2009. 117-125 Papel presentado en Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009.

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

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AU - Cozman, Fabio Gagliardi

AU - Polastro, Rodrigo Bellizia

PY - 2009/12/1

Y1 - 2009/12/1

N2 - This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.

AB - This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.

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UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=80053165440&origin=inward

M3 - Conference Paper

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T2 - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

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Complexity analysis and variational inference for interpretation-based probabilistic description logics

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