Classifying Problem in Inference Engine for Different Version Based Ontology

An ability of reasoning capability of inference engines a useful to derive new & useful information from existing knowledge-base. Classifying algorithms is use to classifying an ontology which improves quality of search on web. Currently Inference engines are able to classify the small ontology completely. For Large and complex version based ontology size is been increase
practically. The main aim of paper is necessary to evaluating the performance of inference engine which focus on classification parameter for large and complex version based ontology for
different domain. Result might be useful to select inference engine for practically on version based ontology for different domain.

View full paper: IJCSI_Classifying Problem in Inference Engine for Different Version Based Ontology

PERFORMANCE EVALUATION of SEMANTIC REASONERS

Abstract- 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 pre-built 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.

View full paper: west2013_submission_45