Proceedings of the 2nd international conference on Knowledge capture
Large scale colour ontology generation with XO
AOW '05 Proceedings of the 2005 Australasian Ontology Workshop - Volume 58
Decidability of SHIQ with complex role inclusion axioms
Artificial Intelligence
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
Ordering heuristics for description logic reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
CEL: a polynomial-time reasoner for life science ontologies
IJCAR'06 Proceedings of the Third international joint conference on Automated Reasoning
An Ontology-Based Condition Analyzer for Fault Classification on Railway Vehicles
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Knowledge engineering rediscovered: towards reasoning patterns for the semantic web
Proceedings of the fifth international conference on Knowledge capture
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This paper presents an empirical evaluation of description logic reasoners to support the selection of scalable ontology engineering patterns for TBox reasoning. Our main objective is to define the rationale behind the design decisions required for the generation of large ontologies with XSLT-based tools. We discuss here the outcomes of an experiment focusing on aircraft components and parts for which we have implemented the ontology design guidelines for part-whole relationships published by W3C's Semantic Web best practices working group. We have worked with the following reasoners, being the best state-of-the-art currently available: FaCT++, RACER, Pellet and CEL. We found considerable variation in reasoner performance and have attempted to characterise the factors that distinguish the reasoners to enable a best-practice design style to be successfully applied for the generation of very large ontologies.