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...

• Type: Journal
• Year: 2016
• Where: JAIR
• Authors: Ana Armas Romero, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
• Keywords: JAIR 2016, Software Engineering