A Rough Sets Approach for Relevant Internet/Web Online Searching
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:63605", author = "Erika Martinez Ramirez and Rene V. Mayorga", title = "A Rough Sets Approach for Relevant Internet/Web Online Searching", 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.", volume = "3", number = "9", pages = "2296-10", }
