Effective and fast module extraction for nonempty ABoxes

A deductive module of a knowledge base KB is a subset of KB that preserves a specified class of consequences. Module extraction is applied in ontology design, debugging, and reasoning. The locality-based module extractors of the OWL API are less effective when the knowledge base contains facts such as ABox assertions. The competing module extractor PrisM computes smaller modules at the cost of higher computation time. In this paper, we introduce and study a novel module extraction technique, called conditional module extraction , that can be applied to satisfiable SRIQ ( D ) knowledge bases. Experimental analysis shows that conditional module extraction constitutes an appealing alternative to PrisM and to the locality-based extractors of the OWL API, when the ABox is nonempty.

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Publication Details

Journal
Artificial Intelligence
Published
2025-04-25
DOI
https://doi.org/10.1016/j.artint.2025.104345
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Effective and fast module extraction for nonempty ABoxes

Iliana M. Petrova, Piero A. Bonatti, L. Sauro, Francesco Magliocca
Artificial Intelligence
Medical Imaging Techniques and Applications
article

Effective and fast module extraction for nonempty ABoxes

Iliana M. Petrova, Piero A. Bonatti, L. Sauro, Francesco Magliocca
article en

Abstract

A deductive module of a knowledge base KB is a subset of KB that preserves a specified class of consequences. Module extraction is applied in ontology design, debugging, and reasoning. The locality-based module extractors of the OWL API are less effective when the knowledge base contains facts such as ABox assertions. The competing module extractor PrisM computes smaller modules at the cost of higher computation time. In this paper, we introduce and study a novel module extraction technique, called conditional module extraction , that can be applied to satisfiable SRIQ ( D ) knowledge bases. Experimental analysis shows that conditional module extraction constitutes an appealing alternative to PrisM and to the locality-based extractors of the OWL API, when the ABox is nonempty.

Artificial IntelligenceVol. 344
Institut national de recherche en sciences et technologies du numérique (FR), University of Naples Federico II (IT)
Ministero dell’Istruzione, dell’Università e della Ricerca
Openalex Percentile: Top 87%
Medical Imaging Techniques and Applications
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