The Bayesian Description Logic BALC

Botha, Leonard and Meyer, Thomas and Penaloza, Rafael (2018) The Bayesian Description Logic BALC, Proceedings of 31st International Workshop on Description Logics, 27-29 October 2018, Phoenix, Arizona, USA, 2211, CEUR.

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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 (prob- abilistic) independence between axioms. In this paper we combine the classical DL ALC with Bayesian Networks. Our new DL includes a so- lution 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 satis- fiability and partial concept satisfiability respectively), knowledge base consistency, subsumption, and instance checking. We show that all rea- soning 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.

Item Type: Conference paper
Uncontrolled Keywords: Bayesian Networks Description Logics
Subjects: Computing methodologies > Artificial intelligence
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Date Deposited: 10 Jan 2019
Last Modified: 10 Oct 2019 15:31

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