Comparing SPARQL Tools for Logical Inferencing in Semantic Web
Aman Jolly1, Shailender Kumar2

1Aman Jolly, Assistant Professor, KIET Group of Institutions, Ghaziabad (Uttar Pradesh), India.
2Dr. Shailender Kumar, Assosciate Professor, Delhi Technological University, (Delhi), India.
Manuscript received on 28 April 2019 | Revised Manuscript received on 10 May 2019 | Manuscript Published on 17 May 2019 | PP: 592-596 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F11250476S419/2019©BEIESP
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Abstract: In today’s modern era, where search engines are still using traditional information retrieval system which usually involves string matching and comparison of text and character. Semantic web technologies are considered an only silver bullet that has the potential to lead the transition from keyword-based search to context-based search. Semantic web technology can provide a system where the web of the document can be made machine-understandable. First, this paper projects how a semantic web’s linked data can lead us to more robust and machine understandable system. Second, this research shows how a logical inference can be made in linked data by using SPARQL queries on different SPARQL tools. Third, this paper shows the analysis of different SPARQL tools such as Twinkle 2.0, Jena ARQ 3.5, and Protégé 5.0. A comparative tabular analysis has been evaluated in order to compare the features of these SPARQL tools and describe shortcomings in their present version.
Keywords: Semantic Web, Linked Data, SPARQL Tools, Protégé 5.0, Jena ARQ 3.5, Twinkle 2.0, Logical Inference.
Scope of the Article: Web Technologies