Work

Effective and fast module extraction for nonempty ABoxes

Year: 2025

Type: article

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.

Source: Artificial Intelligence

DOI: 10.1016/j.artint.2025.104345

Authors: Piero Andrea Bonatti, Francesco Magliocca, Iliana Mineva Petrova, Luigi Sauro

Institutions: University of Naples Federico II, Institut national de recherche en sciences et technologies du numérique, Centre Inria d'Université Côte d'Azur

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,260

Funder: Ministero dell’Istruzione, dell’Università e della Ricerca