The Probabilistic Description Logic BALC

Botha, Leonard and Meyer, Thomas and Penaloza, Rafael (2020) The Probabilistic Description Logic BALC, Theory and Practice of Logic Programming, 1-24, Cambridge University Press.

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Abstract

Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A proba- bilistic 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 propositionally closed DL ALC. We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and gen- eral 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:	Journal article (paginated)
Additional Information:	doi:10.1017/S1471068420000460
Subjects:	Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Date Deposited:	01 Sep 2021 09:57
Last Modified:	01 Sep 2021 09:57
URI:	http://pubs.cs.uct.ac.za/id/eprint/1417

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