International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 11, No. 5, 8 Sep. 2019
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An Efficient and Optimized Sematic Web Enabled Framework (EOSWEF) for Google Search Engine Using Ontology
Full Text (PDF, 600KB), PP.40-45
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Author(s)
Index Terms
Semantic web, Ontology, SWOOGLE, EOSWEF, Semantic search
Abstract
Remarkable growth in the electronics and communication field provides ubiquitous services. It also permits to save huge amount of documents on web. As a result, it is very difficult to search a specific and desired information over the Internet. Classical search engines were unable to investigate the content on web intelligently. The tradition searching results has a lot of immaterial information along with desired one as per user query. To overcome from stated problem many modifications are done in traditional search engines to make them intelligent. These search engines are able to analyze the stored data and reflects only appropriate contents as per users query. Semantic Web is an emerging and efficient approach to handle the searching queries. It gathers appropriate information from web pool based on logical reasoning. It also incorporates rule-based system. Semantic web reasonably scrutinizes webs contents using ontology. The learning process of ontology not only intelligently analyze the contents on web but also improves scrutinizing process of search engine. The paper suggests a new keyword-based semantic retrieval scheme for google search engines. The schemes accelerates the performance of searching process considerably with the help of domain-specific knowledge extraction process along with inference and rules. For this, in ontology the prefix keywords and its sematic association are pre-stored. The proposed framework accelerates the efficiency of content searching of google search engine without any additional burden of end users.
Cite This Paper
Vipin Kumar, Arun Kumar Tripathi, Naresh Chandra, "An Efficient and Optimized Sematic Web Enabled Framework (EOSWEF) for Google Search Engine Using Ontology", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.5, pp. 40-45, 2019. DOI:10.5815/ijieeb.2019.05.06
Reference
[1]J. Beal, “Weaknesses of Full text search”, The Journal of Academic Librarianship, vol. 34, Number 5, pp. 438-444, 2008.
[2]J. Beal,, and Technical Services”, vol. 34, Issues 2–3, pp. 74-82, 2010.
[3]Tim Finin , James Mayfield , Anupam Joshi , R. Scott Cost and Clay Fink, “Information Retrieval and the Semantic Web”, IEEE 8th Annual Hawaii International Conference on System Sciences, pp. 1-10, 2005.
[4]Liyang Yu, “Introduction to the Semantic Web and Semantic Web Services”, First Eition, Chapman and Hall/CRC, 2007.
[5]T. Berners-Lee, “Weaving the Web : The Original Design and Ultimate Destiny of the World Wide Web by its Inventor”, Harper: San Francisco, 1999.
[6]L. Chang, W. Haofen, Y. Yong and X. Linhao, “Towards Efficient SPARQL Query Processing on RDF Data”, Tsinghua Science and Technolgy, vol. 15, Issue 6, pp. 613-622, 2013.
[7]Seema Redekar, Vishal Chekkala, Siddhapa Gouda and Swapnil Yalgude, “Web Search Engine Using Ontology Learning” IJIRCCE, vol. 5, Issue 3, 5092-5097, 2017.
[8]Tim Finin, Yun Peng, R. Scott, Cost Joel, ”Swoogle: A Search and Metadata Engine for the Semantic Web", University of Maryland Baltimore County, pp. 652-659, 2011.
[9]Aidan Hogan and Andreas Harth and Jürgen Umrich, Sheila Kinsella, Axel Polleres and Stefan Decker, "Searching and Browsing Linked Data with SWSE: The Semantic Web Search Engine",Journal of Web Semantics, vol. 9, Issue 4, pp:1-55, 2011.
[10]L.A. Barroso, J. Dean, U. Holzle, “Web search for a planet: The Google cluster architecture”, IEEE Micro vol. 23 Issue. 2, pp. 22-28, 2003.
[11]M.M.EI-gayar, N.Mekky and A.Atwan: “Efficient proposed framework for semantic search engine using new semantic ranking algorithm” in IJACSA, vol 6, no. 8, pp. 136-143, 2015.
[12]M. P. Selvan, C. A. Sekar and P. A. Dharshini, “Survey on Web Page Ranking Algorithms”, International Journal of Computer Applications, vol. 41, No. 19, Published by Foundation of Computer Science, pp. 1-7, 2012.