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JAIR
2016

Module Extraction in Expressive Ontology Languages via Datalog Reasoning

3 years 17 days ago
Module Extraction in Expressive Ontology Languages via Datalog Reasoning
Module extraction is the task of computing a (preferably small) fragment M of an ontology O that preserves a class of entailments over a signature of interest ⌃. 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 O w.r.t. ⌃, 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 entailment...
Ana Armas Romero, Mark Kaminski, Bernardo Cuenca G
Added 06 Apr 2016
Updated 06 Apr 2016
Type Journal
Year 2016
Where JAIR
Authors Ana Armas Romero, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
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