Machine Learning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Who the Heck Is the Father of Bob?
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Foundations of Semantic Web Technologies
Foundations of Semantic Web Technologies
The OWL API: A Java API for OWL ontologies
Semantic Web
Semantic Web
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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The ability to draw logical conclusions in ontologies from explicitly given axioms and facts is one of the key advantages of using semantic technologies. Based on the W3C recommendation of the Web Ontology Language (OWL) a variety of reasoners have been developed for this task. Different language profiles, reasoning algorithms, and special-purpose optimisation techniques have brought up reasoners with various strengths and weaknesses. Selecting the most suitable reasoner for a given reasoning scenario thus is a challenge. This paper presents an automatic reasoner selection approach based on machine learning techniques. The most important ontology and query features are identified and used for learning a model that can be used to predict the best performing reasoner for a given request. The approach is implemented as a strategy in a reasoning broker framework called HERAKLES. Using a training set consisting of 187 real-world ontologies found on the Internet, we evaluated four different machine learning techniques. The results show that a machine learning based reasoner selection strategy can predict the best performing reasoner for a given reasoning request with more than 77% accuracy.