Kumar Sharma

Work place: Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

E-mail: kumar.asom@gmail.com

Website:

Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Data Structures and Algorithms

Biography

Kumar Sharma: Mr. Kumar  Sharma holds bachelor & master degree in Computer Application. Presently, he is pursuing Ph.D. degree in the Department of Computer Science & Engineering, University of Kalyani, West Bengal, India. His research interests include Semantic Web, Ontology, Web Technologies and Big Data. He also has vast experience in mobile (iOS) application development in the field of Education, Point of Sale, and utility applications.

Author Articles
RDF Link Generation by Exploring Related Links on the Web of Data

By Kumar Sharma Ujjal Marjit Utpal Biswas

DOI: https://doi.org/10.5815/ijitcs.2018.10.08, Pub. Date: 8 Oct. 2018

Interlinking RDF resources is a vital aspect of the Semantic Web technology. It is the basis of Linked Data that provides interlinked datasets on the web. One of the principles of Linked Data is interlinking resources from different data sources on the web. Data interlinking is a critical and challenging problem that every Linked Data generation applications face. Various approaches have been evolved for resolving this problem, but, for more massive datasets, it becomes almost indefinite time while linking similar or related resources. Linking RDF resources is like the problem of entity matching, record matching or duplicate resource detection. More or less they attempt to point to the same problem, but the RDF link generation is the task of finding related resources on the web. In this article, we present an approach for generating RDF links using the similarity measure between two RDF resources and by exploring associated relationships of the matched resources. The idea is to find related resources and link them with an RDF resource that is being generated.

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PTSLGA: A Provenance Tracking System for Linked Data Generating Application

By Kumar Sharma Ujjal Marjit Utpal Biswas

DOI: https://doi.org/10.5815/ijitcs.2015.04.10, Pub. Date: 8 Mar. 2015

Tracking provenance of RDF resources is an important task in Linked Data generating applications. It takes on a central function in gathering information as well as workflow. Various Linked Data generating applications have evolved for converting legacy data to RDF resources. These data belong to bibliographic, geographic, government, publications, and cross-domains. However, most of them do not support tracking data and workflow provenance for individual RDF resources. In such cases, it is required for those applications to track, store and disseminate provenance information describing their source data and involved operations. In this article, we introduce an approach for tracking provenance of RDF resources. Provenance information is tracked during the conversion process and it is stored into the triple store. Thereafter, this information is disseminated using provenance URIs. The proposed framework has been analyzed using Harvard Library Bibliographic Datasets. The evaluation has been made on datasets through converting legacy data into RDF and Linked Data with provenance. The outcome has been quiet promising in the sense that it enables data publishers to generate relevant provenance information while taking less time and efforts.

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Other Articles