A Bayesian Extension of the Description Logic ALC

Botha, Leonard and Meyer, Thomas and Penaloza, Rafael (2019) A Bayesian Extension of the Description Logic ALC, Logics in Artificial Intelligence 16th European Conference, JELIA 2019 Rende, Italy, May 7–11, 2019 Proceedings, LNAI, 11468, Springer.

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Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension BALC of the DL ALC. We present a tableau-based procedure for deciding consistency, and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical ALC.

Item Type: Book chapter
Subjects: Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Date Deposited: 15 Jan 2020 11:28
Last Modified: 15 Jan 2020 11:28
URI: http://pubs.cs.uct.ac.za/id/eprint/1364

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