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• W2291693177 abstract "Module extraction is the task of computing a (preferably small) fragment M of an ontology T that preserves a class of entailments over a signature of interest S. Extracting modules of minimal size is well-known to be computationally hard, and often algorithmically infeasible, especially for highly expressive ontology languages. Thus, practical techniques typically rely on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations ensure that M preserves all second-order entailments of T w.r.t. S, which is a stronger condition than is required in many applications, and may lead to unnecessarily large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach generalises existing approximations in an elegant way. More importantly, it allows extraction of modules that are tailored to preserve only specific kinds of entailments, and thus are often significantly smaller. Our evaluation on a wide range of ontologies confirms the feasibility and benefits of our approach in practice." @default.
• W2291693177 created "2016-06-24" @default.
• W2291693177 creator A5023732514 @default.
• W2291693177 creator A5035562013 @default.
• W2291693177 creator A5060350556 @default.
• W2291693177 creator A5080366682 @default.
• W2291693177 date "2016-02-29" @default.
• W2291693177 modified "2023-10-12" @default.
• W2291693177 title "Module Extraction in Expressive Ontology Languages via Datalog Reasoning" @default.
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• W2291693177 doi "https://doi.org/10.1613/jair.4898" @default.
• W2291693177 hasPublicationYear "2016" @default.
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