Abstract

Web Search engine plays a vital role in getting the information proficiently for the user needs from the immense web data. The proposed Genetic Page Rank algorithm (GPRA) based on Google’s Page Rank algorithm (PRA) for the Searching purpose. Using the proposed method we are calculating the ranking for the WebPages. Genetic Algorithm (GA) is applied for ranking Web Pages in which the two parameters: Mutation is used as a similar words (synonyms) and Crossover is consider as a concept. The proposed method gives the result set for the users given query by displaying the synonym words from the Word net database and it ranks the webpage by taking into consideration that number of times a given keyword and their synonym words appears in that webpage as well as the hyperlink count for that keyword and also for the synonym words.

References

1. Dilip Kumar Sharma et al., “A Comparative Analysis of Web Page Ranking Algorithms” in proceedings of the International journal on Computer Science and Engineering(IJCSE), 2010
2. L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web”, Technical Report, Stanford Digital Libraries SIDL-WP- 1999-0120, 1999.
3. Kleinberg J. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999, 46(5): 604-632.
4. Wenpu Xing and Ali Ghorbani, “Weighted PageRank Algorithm”, In proceedings of the 2nd Annual Conference on Communication Networks & Services Research, PP. 305- 314,2004
5. Ricardo Baeza-Yates and Emilio Davis, 'Web page ranking using link attributes', In proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, PP.328-329, 2004
6. Ko Fujimura, Takafumi Inoue and Masayuki Sugisaki,, “The EigenRumor Algorithm for Ranking Blogs”, In WWW 2005 2nd Annual Workshop on the Weblogging Ecosystem, 2005.
7. Ali Mohammad Zareh Bidoki and Nasser Yazdani, “Distance Rank: An Intelligent Ranking Algorithm for Web Pages”, Information Processing and Management, 2007.
8. H Jiang et al., 'TIMERANK: A Method of Improving Ranking Scores by Visited Time', In proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July2008.
9. Shen Jie, Chen Chen, Zhang Hui, Sun Rong-Shuang, Zhu Yan and He Kun, 'TagRank: A New Rank Algorithm for Webpage Based on Social Web' In proceedings of the International Conference on Computer Science and Information Technology, 2008
10. Svenson, Martenson and Micheal Malm et al., “Swarm Intelligence for logistics: Background”, 2004.

Download PDF
Back