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

Research output: Contribution to conference › Conference Paper

20 Scopus citations

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.

Original language	American English
Pages	117-125
Number of pages	9
State	Published - 1 Dec 2009
Externally published	Yes
Event	Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Duration: 1 Dec 2009 → …

Conference

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

Cite this

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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.",

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year = "2009",

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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.

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

Abstract

Conference

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