Tractable reasoning via approximation
Artificial Intelligence
Evaluating Optimized Decision Procedures for Propositional Modal K(m) Satisfiability
Journal of Automated Reasoning
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
What Is Approximate Reasoning?
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
A new general method to generate random modal formulae for testing decision procedures
Journal of Artificial Intelligence Research
Modular reuse of ontologies: theory and practice
Journal of Artificial Intelligence Research
Finding all justifications of OWL DL entailments
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Ontology performance profiling and model examination: first steps
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
JustBench: a framework for OWL benchmarking
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
CEL: a polynomial-time reasoner for life science ontologies
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
The modular structure of an ontology: atomic decomposition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Consequence-based and fixed-parameter tractable reasoning in description logics
Artificial Intelligence
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Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them "hot spots". The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.