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The Probabilistic Description Logic

Published online by Cambridge University Press:  11 December 2020

LEONARD BOTHA ,
THOMAS MEYER Open the ORCID record for THOMAS MEYER [Opens in a new window]  and
RAFAEL PEÑALOZA Open the ORCID record for RAFAEL PEÑALOZA [Opens in a new window]
LEONARD BOTHA
Affiliation:
University of Cape Town and CAIR, South Africa
THOMAS MEYER
Affiliation:
University of Cape Town and CAIR, South Africa
RAFAEL PEÑALOZA
Affiliation:
University of Milano-Bicocca, Italy (e-mail: rafael.penaloza@unimib.it)
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Abstract

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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 .

Keywords

knowledge representation and nonmonotonic reasoningdatabases and semantic web reasoning

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 4: TPLP Special Issue from the 16th European Conference on Logics in Artificial Intelligence (JELIA 2019) , July 2021 , pp. 404 - 427
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Footnotes

*

Part of this work was carried out while this author was at the Free University of Bozen-Bolzano, Italy.

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