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Number 9

Improving Web Search with the EWEBSEARCH Model

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  • Abur M.m. and Adewale S. O. and Hammawa M. B. and Soroyewun M. B. 2012. Improving Web Search with the EWEBSEARCH Model. International Journal of Applied Information Systems. 3, 9 (August 2012), 7-11. DOI=http://dx.doi.org/10.5120/ijais450498
  • @article{10.5120/ijais2017451568,
    author = {Abur M.m. and Adewale S. O. and Hammawa M. B. and Soroyewun M. B.},
    title = {Improving Web Search with the EWEBSEARCH Model},
    journal = {International Journal of Applied Information Systems},
    issue_date = {August 2012},
    volume = {3},
    number = {},
    month = {August},
    year = {2012},
    issn = {},
    pages = {7-11},
    numpages = {},
    url = {/archives/volume3/number9/257-0498},
    doi = { 10.5120/ijais12-450498},
    publisher = { xA9 2010 by IJAIS Journal},
    address = {}
    }
    
  • %1 450498
    %A Abur M. M. 
    %A Adewale S.  O.
    %A Hammawa M.  B.
    %A Soroyewun M.  B.
    %T Improving Web Search with the EWEBSEARCH Model
    %J International Journal of Applied Information Systems
    %@ 
    %V 3
    %N 
    %P 7-11
    %D 2012
    %I  xA9 2010 by IJAIS Journal
    

Abstract

Traditional web which is the largest information database lacks semantic and as a result the information available in the web is only human understandable, not by machine. With the rapid increase in the amount of information on networks, search engine has become the infrastructure for people gaining access to Web information, and is the second largest Internet application besides e-mail. However, search engine returns a huge number of results, and the relevance between results and user queries is also different. There are lots of search engines available today, but the way to retrieve meaningful information is difficult. To overcome this problem in search engines to retrieve meaningful information intelligently or smartly, Semantic Web technology has played a major role. In the light of this, our paper, proposes an algorithm, architecture for the semantic web based search engine named EWEBSEARCH model, powered by XML meta-tags (which ensures machine understandability) to improve web search. The EWEBSEARCH model provides a simple interface to capture user's queries (keywords), then the search or query engine processes the queries from the repository (database) using the search engine algorithm, interpreting the queries, retrieving and providing appropriate ranking of results in order to satisfy users queries. Query answers are ranked using extended information-retrieval techniques, are generated in an order of ranking and implementation of the model.

References

  1. Abur M. M. , Enhancing Web Search using Semantic Web Technology; M. Sc. Thesis; Ahmadu Bello University ABU, Zaria Nigeria, 2012.
  2. Alhassan Adamu (2011) thesis work: the Implementation of Semantic Web methods to Search engines.
  3. Antoniou, G. & Harmelen F. V. (2008), A Semantic Web primer 2nd edition.
  4. Berners-lee, T. (1997). "Metadata architecture. " http://www. w3. org/ Design Issues/Metadata. html,Jaunary 1997.
  5. Berners-lee, T. , Hendler, J. & lassila O. (2001). The Semantic web, Scientific American, May 2001 pp. 29-37
  6. "Bing Search Engine". http://www. bing. com
  7. Evri: About Us. 2009 http://www. evri. com/about. html>.
  8. "Google Search Engine". http://www. google. com
  9. Ledford J. L;(2008) Search Engine Optimization Bible. Wiley Publishing, Inc
  10. Levene M. , An introduction to Search Engines and Web Navigation. (2010), second edition.
  11. Lyndon N. and Elena P. (2004), State of the art of current Semantic Web Services initiatives
  12. Manning C. D. , Raghavan P. SchxFCtze H. , (2009) An Introduction to Information Retrieval Online edition.
  13. Pollock J. T. , (2009) Semantic Web for Dummies.
  14. Sensebot semantic web search engine (2010).
  15. TxFCmer D. , Shah M. A. , and Bitirim Y. , An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google, Yahoo, Msn and Hakia, 2009 4th International Conference on Internet Monitoring and Protection (ICIMP '09).
  16. "Yahoo Search Engine". http://www. yahoo. com

Keywords

Database, EWEBSEARCH model, Search engine, Semantic Web, XML meta-tags

Index Terms

Computer Science
Information Sciences