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Optimizing Search Engine Result using Intelligent Model

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 1
Year of Publication: 2012
Authors:
Hamada. M. Zahera
Gamal. F. Elhady
Arabi. E. Keshk
10.5120/7736-0788

Hamada. M.zahera, Gamal. F Elhady and Arabi. E Keshk. Article: Optimizing Search Engine Result using Intelligent Model. International Journal of Computer Applications 50(1):30-37, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Hamada. M.zahera and Gamal. F. Elhady and Arabi. E. Keshk},
	title = {Article: Optimizing Search Engine Result using Intelligent Model},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {1},
	pages = {30-37},
	month = {July},
	note = {Full text available}
}

Abstract

Many search engine users face problems while retrieving their required Information. For example, a user may find it is difficult to retrieve sufficient relevant information because he use too few keywords to search or the user is inexperienced and do not search using proper keywords and the search engine is not able to receive the user real meaning through his given keywords. Also, due to the recent improvements of search engines and the rapid growth of the web, the search engines return a huge number of web pages, and then the user may take long time to look at all of these pages to find his needed information. The problem of obtaining relevant results in web searching has been tackled by several approaches. Although very effective techniques are currently used by the most popular search engines, but no a priori knowledge on the user's desires beside the search keywords is available. In this paper, we present an approach for optimizing the search engine results using artificial intelligence techniques such as document clustering and genetic algorithm to provide the user with the most relevant pages to the search query. The proposed method uses the Meta-data that is coming from the user preferences or the search engine query log files. These data is important to find the most related information to the user while searching the web. Finally, the method implementation and some of the experimental results are presented with the conclusion of this research study.

References

  • W. Lee and T. Tsai, "An interactive agent-based system for concept-based web search"," Expert Systems with Applications, vol. 24, pp. 365-373, 2003.
  • R. Yates, "Information retrieval in the Web: beyond current search engines," International Journal of Approximate Reasoning, vol. 34, no. 3, November 2003.
  • L. Chen, C. Luh, and C. Jou, "Generating page clippings from web search results using a dynamically terminated genetic algorithm," Elsevier Information Systems, vol. 30, pp. 299-316, 2005.
  • R. L. Cecchini, C. M. Lorenzetti, A. G. Maguitman, and N. B. Brignole, "Using genetic algorithms to evolve a population of topical queries," Elsevier Information Processing and Management, vol. 44, pp. 1863-1878, 2008.
  • A. Trotman, "An artificial intelligence approach to information retrieval," in Information Processing and Management, 2004 , pp. 619-632.
  • A. Leuski, "Evaluating document clustering for interactive information retrieval. ," in Proceedings of the 2001 ACM CIKM International Conference on Information and Knowledge Management, Atlanta, Georgia, USA, 2001, pp. 33–44.
  • M Caramia, G Felici, and A Pezzoli, "Improving search results with data mining in a thematic search engine," Computers & Operations Research, pp. 2387–2404, 2004.
  • A. A. Radwan, B. A. Abdel Latef, A. A. Ali, and Osman A. Sadek, "Using Genetic Algorithm to Improve Information Retrieval Systems," in word academy of science, Engineering and Technology, 2006, p. 17.
  • M Gorden, "Probabilistic and genetic algorithms in document retrieval," Communications of the ACM, vol. 31, no. 10, pp. 1208–8, October 1988.
  • M. Gordon, "User-based document clustering by redescribing subject descriptions with a genetic algorithm," Journal of the American Society for Information Science, vol. 42, no. 5, pp. 311–22, 1991.
  • F. Dashti and S. A. Zad, "Optimizing the data search results in web using Genetic Algorithm, "International Journal of Advanced Engineering Sciences and Technologies" vol. 1, no. 1, pp. 016 – 022, 2010.
  • A. Al-Dallal and R. S. Abdul-Wahab, "Genetic Algorithm Based to Improve HTML Document Retrieval," in Developments in eSystems Engineering, Abu Dhabi , 2009, pp. 343 - 348.
  • Z. S. Ma , and Q. He, "Web Mining: Extracting Knowledge from the World Wide Web," in Data Mining for Business Applications, Longbing Cao et al. , Eds. : Springer, 2009, ch. 14, pp. 197-208.
  • E. Agichtein, E. Brill, and S. Dumais, "Improving web search ranking by incorporating user behavior information," in SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, New York, 2006.
  • Y. Du and H. Li, "An Intelligent Model and Its Implementation of Search Engine," Journal of Convergence Information Technology, vol. 3, no. 2, pp. 57-66, June 2008.
  • G. Salton and M. H McGill, Introduction to Modern Information Retrieval. , 1983.
  • T. Huang, Y. Liaw and J. . C. Lai, "A fast -means clustering algorithm using cluster center displacement," Pattern Recognition, vol. 42, no. 11, pp. 2551-2556, November 2009.
  • F Picarougne, N Monmarchle, A. Oliver, and G. Venturini, "Web mining with a genetic algorithm," In Proceedings of the Eleventh International, 2002.
  • W. Fan, P. Pathak, and Mi Zhou, "Genetic-based approaches in ranking function discovery and optimization in information retrieval — A framework," Decision Support Systems, vol. 47, no. 4, pp. 398–407, November 2009.
  • W. Fan, E. A. Fox, P. Pathak, and H. Wu, "The effects of fitness functions on genetic programming-based ranking discovery for web search," Journal of the American Society for Information Science and Technology, vol. 55, pp. 628–636. , 2004.
  • W. Shengli, B. Yaxin, and Zeng, Xiaoqin, "Using the Euclidean Distance for Retrieval Evaluation," in Advances in Databases, Alvaro Fernandes, Alasdair Gray, and Khalid Belhajjame, Eds. Berlin / Heidelberg, German: Springer , 2011, pp. 83-96.
  • D. Hawking, N. Craswell, P. Bailey, and K. Griffihs, "Measuring Search Engine Quality," Information Retrieval, vol. 4, no. 1, pp. 33-59, 2001.