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dc.contributor.author Botha, L
dc.contributor.author Meyer, Thomas
dc.contributor.author Penaloza, R
dc.date.accessioned 2019-03-25T14:07:22Z
dc.date.available 2019-03-25T14:07:22Z
dc.date.issued 2018-10
dc.identifier.citation Botha, L. Meyer, T. and Penaloza, R. 2018. The Bayesian Description Logic BALC. DL 2018 - 31st International Workshop on Description Logics, Tempe, Arizona, 27-29 October 2018 en_US
dc.identifier.uri http://ceur-ws.org/Vol-2211/paper-09.pdf
dc.identifier.uri http://hdl.handle.net/10204/10848
dc.description Paper presented at DL 2018 - 31st International Workshop on Description Logics, Tempe, Arizona, 27-29 October 2018 en_US
dc.description.abstract Description Logics (DLs) that support uncertainty are not as well studied as their crisp alternatives, thereby limiting their use in real world domains. The Bayesian DL BEL and its extensions have been introduced to deal with uncertain knowledge without assuming (probabilistic) independence between axioms. In this paper we combine the classical DL ALC with Bayesian Networks. Our new DL includes a solution to the consistency checking problem and changes to the tableaux algorithm that are not a part of BEL. Furthermore, BALC also supports probabilistic assertional information which was not studied for BEL. We present algorithms for four categories of reasoning problems for our logic; two versions of concept satisfiability (referred to as total concept satisfiability and partial concept satisfiability respectively), knowledge base consistency, subsumption, and instance checking. We show that all reasoning problems in BALC are in the same complexity class as their classical variants, provided that the size of the Bayesian Network is included in the size of the knowledge base. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Worklist;22165
dc.subject Bayesian Description Logic en_US
dc.subject BALC en_US
dc.subject Bayesian networks en_US
dc.title The Bayesian Description Logic BALC en_US
dc.type Presentation en_US


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