A Rough Sets Approach for Relevant Internet/Web Online Searching

Authors: Erika Martinez Ramirez, Rene V. Mayorga

Abstract:

The internet is constantly expanding. Identifying web links of interest from web browsers requires users to visit each of the links listed, individually until a satisfactory link is found, therefore those users need to evaluate a considerable amount of links before finding their link of interest; this can be tedious and even unproductive. By incorporating web assistance, web users could be benefited from reduced time searching on relevant websites. In this paper, a rough set approach is presented, which facilitates classification of unlimited available e-vocabulary, to assist web users in reducing search times looking for relevant web sites. This approach includes two methods for identifying relevance data on web links based on the priority and percentage of relevance. As a result of these methods, a list of web sites is generated in priority sequence with an emphasis of the search criteria.

Keywords: Web search, Web Mining, Rough Sets, Web Intelligence, Intelligent Portals, Relevance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083293

References:

[1] Boose, J.H. and Gaines, B.R., Knowledge acquisition tools for expert systems. London; Toronto : Academic Press, c1988. ISBN. 0122732510 (vol.2).
[2] Chang, C.-H. and Hsu, C.-C., Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW IEEE Transactions on Knowledge and Data Engineering, vol. 11, pp. 595-609, 1999.
[3] Chekuri, C., Goldwasser, M. H., Raghavan, P., and Upfal, E., Web search using automatic classification In Proc. of the 6th International World Wide Web Conference (WWW), vol. 1997.
[4] De Cock, M. and Cornelis, C., Fuzzy Rough Set Based Web Query Expansion in: Proceedings of Rough Sets and Soft Computing in Intelligent Agent and Web Technology, International Workshop at WIIAT2005 (2005 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology), p. 9-16, 2005
[5] Frigui, H. and Nasraoui, F., A fast algorithm for discovering categories and attribute relevance in web data In Proc on Fuzzy Information Processing Society (NAFIPS. 2002) Annual Meeting of the North American, vol. pp. 280-285, Jun, 2002.
[6] He, D., A Study of Self-Organizing Map in Interactive Relevance Feedback 3rd Intl. Conf. on Information Technology: New Generations (ITNG 2006), vol. pp. 394-401, Apr, 2006.
[7] Hiemstra, D. and Robertson, S., Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval In A. F. Smeaton and J. Callan, editors, Proceedings of the Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries, vol. pp. 37-42, Jun, 2001.
[8] Jang J.-S. R., Sun C.-T. Mizutani E. Neuro-Fuzzy and Soft Computing: A computational approach to learning and machine intelligence. Matlab Curriculum Series. Edit. Prentice Hall. 1997.
[9] Komorowski, J., Pawlak, Z., Polkowski, L., and Skowron, A., Rough Sets: A Tutorial In: Pal, S.K., Skowron, A. (eds.): Rough Fuzzy Hybridization - A New Trend in Decision-Making , vol. pp. 3-98, 1999.
[10] Lingras P. Rough set clustering for web mining In Proceedings of 2002 World Congress on Computational Intelligence, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE(02) Special Session on Computational Web Intelligence (CWI), vol. pp. 1039-44, 2002.
[11] Martinez E. Mayorga R. V., "An Architecture for the Coupling of Intelligent Computer Interfaces with Intelligent Systems: An Online Internet Portals Customization Application. Proceedings 4th ANIROB/IEEE-RAS Intl. Symposium on Robotics and Automation, Queretaro, Mexico, August, 25-27, 2004
[12] Mock, K., Dynamic email organization via relevance categories In Proc. 11th Intl. Conf. on Tools with Artificial Intelligence, vol. pp. 399- 405, Nov, 1999.
[13] Ngo C. L. and Nguyen, H. S., A method of web search result clustering based on rough sets Proceedings of 2005 IEEE/WIC/ACM International Conference on Web Intelligence, vol. pp. 673-679, Sep, 2005.
[14] Pawlak, Z.. Rough sets : theoretical aspects of reasoning about data. Dordrecht ; Boston : Kluwer Academic Publishers, c1991. ISBN. 0792314727 (HB : acid free paper)
[15] Rojanavasu, P., Pinngern, 0. Extended Rough Fuzzy Sets For Web Search Agent, Proceedings of the 25'h International Conference on Information Technology Interfaces June 16-19, 2003, Cavtat, Croatia pp. 403-407
[16] Rui, Y. and Huang, T. S., A novel relevance feedback technique in image retrieval In Proc. ACM Multimedia , vol. pp. 67-70, 1999.
[17] Shen, D., Chen, Z., Yang, Q., Zeng, H.-J., Zhang, B., Lu, Y., and Ma, W.-Y., Web-page classification through summarization In Proc. of the 27th annual international conference on Research and development in information retrieval, vol. pp. 242-249, 2004.
[18] Takama, Y., Consideration of Relevance Feedback on Keyword Space for Interactive Information Retrieval IEEE Conference on Cybernetics and Intelligent Systems (CIS2004), vol. 1, pp. 324-328, 2004.
[19] Wu, Y. and Zhang, A., An Adaptive Classification Method for Multimedia Retrieval "", in IEEE International Conference on Multimedia and Expo (ICME'03), vol. pp. 757-760, Jul, 2003.
[20] Yi, G., Hu, H., and Lu, H., Web Document Classification Based on Extended Rough set Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2005), vol. pp. 916-919, Dec, 2005.