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A Study on the Issues Hindering the Semantic Web Adoption and a Proposition of an Effective Machine Learning Approach for it

by Pranal Pradeep Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 28
Year of Publication: 2018
Authors: Pranal Pradeep Kumar
10.5120/ijca2018918114

Pranal Pradeep Kumar . A Study on the Issues Hindering the Semantic Web Adoption and a Proposition of an Effective Machine Learning Approach for it. International Journal of Computer Applications. 181, 28 ( Nov 2018), 17-18. DOI=10.5120/ijca2018918114

@article{ 10.5120/ijca2018918114,
author = { Pranal Pradeep Kumar },
title = { A Study on the Issues Hindering the Semantic Web Adoption and a Proposition of an Effective Machine Learning Approach for it },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 181 },
number = { 28 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 17-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number28/30115-2018918114/ },
doi = { 10.5120/ijca2018918114 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:29.662711+05:30
%A Pranal Pradeep Kumar
%T A Study on the Issues Hindering the Semantic Web Adoption and a Proposition of an Effective Machine Learning Approach for it
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 28
%P 17-18
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The web nowadays is a dynamic container of ever-increasing data. People, businesses, and devices have all become data factories that are pumping out incredible volumes of information to the web each day. There are 2.5 quintillion bytes of data created each day at our current pace on the web[1]. Also, 90% of the data produced is unstructured. Hence, there is a pressing need for the transformation of the Web 2.0 and thereby bringing in the concept of Semantic Web with a rapid pace. But, the implementation of Web 3.0 is being decelerated by various issues. However, recent advancements in the field of Machine Learning have proposed approaches to bridge the gap between semantics and the current web. In this paper, various challenges that hinder the adoption of Semantic Web and the new opportunities especially in the field of machine learning that can provide a thrust to this process have been explored.

References
  1. Micro Focus Blog, “Data on the internet each day”, https://blog.microfocus.com/
  2. Tim Berners-Lee, Mark Fischetti, Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor, Harper San Francisco, 1999.
  3. W3C website, “Resource Description Framework (RDF): Concepts and Abstract Data Model”, http://www.w3.org/TR/2002/WD-rdf-concepts-20020829/
  4. Devopedia,"Semantic Web." Version 3, May 29, https://devopedia.org/semantic-web
  5. Shalini Batra, Seema Bawa, "Review of Machine Learning Approaches to Semantic Web Service Discovery", Journal of Advances in Information Technology, 2010.
  6. Nicole Oldham, Christopher Thomas, Amit P. Sheth, and Kunal Verma, “METEOR-S Web service annotation framework with machine learning classification”, In Semantic Web Services and Web Process Composition, volume 3387 of LNCS, pages 137–146, San Diego, CA, USA, 2004. Springer
Index Terms

Computer Science
Information Sciences

Keywords

Semantic Web Ontology Mining Machine Learning

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