NDLI: Automatic reasoner selection using machine learning

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Automatic reasoner selection using machine learning

Content Provider	ACM Digital Library
Author	Lösch, Uta Bock, Jürgen Wang, Hai
Abstract	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.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISBN	9781450309158
DOI	10.1145/2254129.2254159
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ontology reasoning Machine learning Logics Brokerage
Content Type	Text
Resource Type	Article

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