Ontology module extraction via datalog reasoning

Conference item

Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to 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 not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.

Authors: Romero, AA; Kaminski, M; Grau, BC; Horrocks, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: AAAI Press
• Host title: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence and the Twenty-Seventh Innovative Applications of Artificial Intelligence Conference, January 25–30, 2015, Austin, Texas, USA
• Volume: 1
• Pages: 1410-1416
• Publication date: 2015-01-25
• Event title: AAAI-15: Twenty-Ninth Conference on Artificial Intelligence
• Event location: Austin, Texas, USA
• Event website: https://aaai.org/conference/aaai/aaai15/
• Event start date: 2015-01-25
• Event end date: 2015-01-30
• EISSN: 2374-3468
• ISSN: 2159-5399
• ISBN: 978-0-262-51129-2
• Language: English
• Keywords: ontologies; datalog reasoning; module extraction