A Framework for Reasoning on Probabilistic Description Logics

Applications and Practices in Ontology Design, Extraction, and Reasoning - Studies on the Semantic Web ◽

10.3233/ssw200040 ◽

2020 ◽

Author(s):

Giuseppe Cota ◽

Riccardo Zese ◽

Elena Bellodi ◽

Evelina Lamma ◽

Fabrizio Riguzzi

Keyword(s):

Description Logics ◽

Uniform Method ◽

Desktop Application ◽

Probabilistic Description ◽

Probabilistic Queries

While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.

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Semantics and Inference for Probabilistic Description Logics

Uncertainty Reasoning for the Semantic Web III - Lecture Notes in Computer Science ◽

10.1007/978-3-319-13413-0_5 ◽

2014 ◽

pp. 79-99 ◽

Cited By ~ 3

Author(s):

Riccardo Zese ◽

Elena Bellodi ◽

Evelina Lamma ◽

Fabrizio Riguzzi ◽

Fabiano Aguiari

Keyword(s):

Description Logics ◽

Probabilistic Description

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A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics

Algorithms ◽

10.3390/a14100280 ◽

2021 ◽

Vol 14 (10) ◽

pp. 280

Author(s):

Rafael Peñaloza

Keyword(s):

Artificial Intelligence ◽

Knowledge Representation ◽

Classical Logic ◽

Description Logics ◽

Building Blocks ◽

Main Building ◽

Probabilistic Description ◽

The Family ◽

Uncertain Knowledge ◽

Classical Knowledge

Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.

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Probabilistic Description Logics for Subjective Uncertainty

Journal of Artificial Intelligence Research ◽

10.1613/jair.5222 ◽

2017 ◽

Vol 58 ◽

pp. 1-66 ◽

Cited By ~ 6

Author(s):

Victor Gutierrez-Basulto ◽

Jean Christoph Jung ◽

Carsten Lutz ◽

Lutz Schröder

Keyword(s):

Description Logics ◽

Order Logic ◽

First Order Logic ◽

Two Dimensional ◽

Subjective Probabilities ◽

Subjective Uncertainty ◽

First Order ◽

New Family ◽

Probabilistic Description

We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable.

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The Probabilistic Description Logic

Theory and Practice of Logic Programming ◽

10.1017/s1471068420000460 ◽

2020 ◽

pp. 1-24

Author(s):

LEONARD BOTHA ◽

THOMAS MEYER ◽

RAFAEL PEÑALOZA

Keyword(s):

Knowledge Representation ◽

Description Logic ◽

Description Logics ◽

Light Weight ◽

Classical Form ◽

First Order ◽

Probabilistic Description

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 .

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