Abstract

Ontologies are emerging as best representation techniques for knowledge based context domains. The continuing need for interoperation, collaboration and effective information retrieval has lead to the creation of semantic web with the help of tools and reasoners which manages personalized information. The future of semantic web lies in an ontology which describes relationship between terms, and will serve as a foundation for establishing a shared understanding between applications. In this paper, we surveyed and compared numerous reasoning models, ontology tools and express well defined Web services for user with different annotations. We compared latest and traditional reasoners like Pellet, RACER, HermiT, FaCT++ with respect to their features supported by them. Similarly, different variety of ontology development, querying and designing tools like Protégé, Jena, SWOOP, Oiled, Apollo, etc. have been compared to predict the inference support through utilizing several features backed up by them. Finally, this paper presents visualized comparison among all reasoners, tools with the aid of their supporting features or characteristics and classified them as strong, average or weak. In addition, we have also classified the reasoner on the basis of their response time and it was observed that Pellet has lowest response time whereas Racer has highest response time.

Citation

Khamparia, A. & Pandey, B. (2017). Comprehensive Analysis of Semantic Web Reasoners and Tools: A Survey. Education and Information Technologies, 22(6), 3121-3145. Retrieved June 9, 2023 from https://www.learntechlib.org/p/191735/.

This record was imported from ERIC on January 9, 2019. [Original Record]

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Keywords

• classification
• Inferences
• information retrieval
• internet
• programming languages
• Reaction Time
• Semantics