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Reseach Article

Relation based Measuring of Semantic Similarity for Web Documents

by Poonam Chahal, Manjeet Singh, Suresh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 7
Year of Publication: 2015
Authors: Poonam Chahal, Manjeet Singh, Suresh Kumar
10.5120/21081-3762

Poonam Chahal, Manjeet Singh, Suresh Kumar . Relation based Measuring of Semantic Similarity for Web Documents. International Journal of Computer Applications. 119, 7 ( June 2015), 26-29. DOI=10.5120/21081-3762

@article{ 10.5120/21081-3762,
author = { Poonam Chahal, Manjeet Singh, Suresh Kumar },
title = { Relation based Measuring of Semantic Similarity for Web Documents },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 7 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number7/21081-3762/ },
doi = { 10.5120/21081-3762 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:26.541268+05:30
%A Poonam Chahal
%A Manjeet Singh
%A Suresh Kumar
%T Relation based Measuring of Semantic Similarity for Web Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 7
%P 26-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The World Wide Web (WWW) is the information resource centre in which information exists in the structure of web pages which are interlinked with each other. From the huge amount of information present on WWW it has been found difficult to extract the relevant information for the query given by the user. The reason for this is that the information exists on web is in natural language. The layered architecture semantic web is given by Tim Berner Lee to overcome the issues of information retrieval. In recent times, numerous semantic web search engines have been developed like Ontolook, Swoogle, etc which assist in searching significant documents presented on semantic web. Several attempts have been made in ruling out the similarity of semantic web pages but then also the results of these semantic similarity techniques between web documents is neither appropriate nor upto the user's prospects. This paper proposes an approach for finding the semantic similarity between the web documents along with the consideration of the concepts as well as the relationships that will exists between the concepts also. In our approach the documents are being processed by extracting concepts and relationships between the existing concepts from the documents using the base ontology and the dictionary having the words along with the synonyms. Finally, the set of any two documents are compared to find their semantic similarity by taking the relationships that exists in the documents. We discover all relevant relationships between the words which provide the core information of the document and then the similarity of these relationships is computed on each web page to find out their significance.

References
  1. Berners-Lee T. , Hendler J. , and O. Lassila, "The Semantic Web," Scientific Am. , 2001.
  2. Brin S. and L. Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine", Proc. Of 7th Int'l Conf. on World Wide Web (WWW '98), pp. 107-117, 1998.
  3. Danushka B. , Matsuo Y. , and Ishizuka M. , " Measuring the Similarity Between Implicit Semantic Relations from the Web", International World Wide Web Conference Committee ACM, pg 651-660, 2009.
  4. Ding L. , Kolari P. , Ding Z. , and S. Avancha, "Using Ontologies in the Semantic Web: A Survey", Ontologies, integrated series of information systems, vol 14, pp. 79-113, Springer, 2007.
  5. Hongzhe Liu and Pengfe Wang, "Assessing text semantic similarity using ontology", Journal of Software, vol 9, No. 2, Feb 2014.
  6. Lee W, Shah N. , Sundlass K. , and Musen M. ,"Comparison of Ontology Based Semantic Similarity Measures", AMIA Annu Symp Proc. , pg 384-388, 2008.
  7. Lee M. , Jia C. , and Tung H. , "A Grammar Based Semantic SimilarityAlgorithm for Natural Language Sentences", Research Article in Scientific World Journal, Pg(s) 17, vol 2014.
  8. Mingxin G, Duo Xue and Jiang Rui," From Ontology to Semantic Similarity: Calculation of Ontology Based Semantic Similarity", Scientific World Journal, pg(s) 11, Vol 2013.
  9. Nagwani N. , and Shrish V. , "A frequent term and semantic similarity based single document text summarization algorithm" International Journal of Computer Applications(0975-8887), vol-17, No. 2, 2011.
  10. Oleshchuk V. , and Asle P. , "Ontology Based Semantic Similarity Comparison of Documents", Proc. of IEEE 14th workshop on database and expert systems applications,2003.
  11. Page L. , S. Brin, R. Motwani, and T. Winograd, "The Page Rank Citation Ranking: Bringing Order to the Web", Stanford Digital Library Technologies Project, 1998.
  12. Pisharody A. and H. E. Michel, "Search Engine Technique Using Keyword Relations", Proc. of Int'l Conf. on Artificial Intelligence(ICAI '05), pp. 300-306, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

WWW Information Retrieval Ranking Semantic Similarity