Interpreting Inference Engine for Semantic Web

Semantic web is a web of data, where data should be related to one another and also Knowledge will be organized in conceptual spaces according to its meaning. To understand and use the data and knowledge encoded in semantic web documents requires inference engine. There are number of inference engines used for consistency checking and classification like Pellet, Fact, Fact++, Hermit, Racer Pro, KaON2, and Base Visor. Some of them are reviewed and tested for few prebuilt ontologies. This paper presents the analysis of different inference engines with set of ontologies. It requires assessment and evaluation before selecting an appropriate inference engine for a given application.