As the performance of semantic reasoners change significantly with respect to all included characteristics, and therefore requires assessment and evaluation before selecting an appropriate reasoner for a given application. There are number of inference engines like Pellet, FaCT++, Hermit, RacerPro, KaON2, F-OWL and BaseVISor. Some of them are reviewed and tested for few prebuilt ontologies. This paper proposes performance evaluation and comparison of semantic reasoner for the ontologies of Health and Anatomy domain. Reasoners are characterized based on reasoning method, reasoning algorithm, computational complexity, classification, scalability, query and rule support.
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