Artificial Intelligence
The Description Logic Handbook
The Description Logic Handbook
The foundational model of anatomy in OWL: Experience and perspectives
Web Semantics: Science, Services and Agents on the World Wide Web
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Consequence-driven reasoning for horn SHIQ ontologies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Sound Global State Caching for ALC with Inverse Roles
TABLEAUX '09 Proceedings of the 18th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
Hypertableau reasoning for description logics
Journal of Artificial Intelligence Research
Blocking and other enhancements for bottom-up model generation methods
IJCAR'06 Proceedings of the Third international joint conference on Automated Reasoning
FaCT++ description logic reasoner: system description
IJCAR'06 Proceedings of the Third international joint conference on Automated Reasoning
Description logic reasoning for semantic web ontologies
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Hybrid reasoning for ontology classification
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
A novel approach to ontology classification
Web Semantics: Science, Services and Agents on the World Wide Web
The Foundational Model of Anatomy in OWL 2 and its use
Artificial Intelligence in Medicine
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers of KES2012-Part 1 of 2
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State of the art reasoners for expressive description logics, such as those that underpin the OWL ontology language, are typically based on highly optimized implementations of (hyper)tableau algorithms. Despite numerous optimizations, certain ontologies encountered in practice still pose significant challenges to such reasoners, mainly because of the size of the model abstractions that they construct. To address this problem, we propose a new blocking technique that tries to identify and halt redundant construction at a much earlier stage than standard blocking techniques. An evaluation of a prototypical implementation in the HermiT reasoner shows that our technique can dramatically reduce the size of constructed model abstractions and reduce reasoning time.