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

Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro

Research output: Contribution to conferenceConference 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 languageAmerican English
Pages117-125
Number of pages9
StatePublished - 1 Dec 2009
Externally publishedYes
EventProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 -
Duration: 1 Dec 2009 → …

Conference

ConferenceProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
Period1/12/09 → …

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