The Probabilistic Description Logic. Issue 4 (11th July 2021)

• Record Type: Journal Article
• Authors: BOTHA, LEONARD; MEYER, THOMAS; PEÑALOZA, RAFAEL
• Is Part Of: Theory and practice of logic programming. Volume 21:Issue 4(2021)
• Page Start: 404
• Page End: 427
• Publication Date: 2021-07-11
• Subjects: knowledge representation and nonmonotonic reasoning -- databases and semantic web reasoning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068420000460
• Languages: English
• ISSNs: 1471-0684

Abstract

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 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 of the propositionally closed DL . 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 .