skip to main content
10.1145/1167350.1167376acmconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
Article

Incorporating agent based neural network model for adaptive meta-search

Published: 18 March 2005 Publication History

Abstract

In the current information age, the web is increasing at a very rapid pace, while the indexes of the current Search Engines are not scaling up at the same pace resulting in the loss of access to a good fraction of documents on the web. An intriguing alternative is a Meta-Search Engine, which provides a unified access to several Search Engines thereby increasing the coverage of the web. Though using Meta-Search Engines, the coverage of the web is increased, maintaining a good precision can be a problem especially if one or more of the Search Engine's returns irrelevant documents for certain user queries. This paper proposes a novel, intelligent, and adaptive approach to improve the precision of the meta-search results. This approach uses an adaptive agent based neural network model to improve the quality of the search results by incorporating user relevance feedback in to the system.

References

[1]
Bergman, M. The Deep Web: Surfacing Hidden Value. Journal of Electronic Publishing, 7(1), 2001.
[2]
Choi, J., Kim, M., and Raghavan, V. V. Adaptive feedback methods in an extended boolean model. In Proceedings of ACM SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, New Orleans, LA, Sept. 2001.
[3]
Fan, Y. and Gauch, S. Adaptive agents for information gathering from multiple, distributed information sources. In proceedings of the 1999 AAAI Symposium on Intelligent agents in Cyberspace, Stanford University (March 1999), pp 40--46.
[4]
Lawrence, S. and Lee Giles, C. Accessibility of information on the web. Nature 400, 107--109.
[5]
Meng, W., Yu, C., and Liu, K. Building Efficient and Effective Metasearch Engines. ACM Computing Surveys, Vol. 34, No. 1, March 2002, pp.48--89.
[6]
Raghavan, S, and Garcia-Molina, H. Crawling the Hidden Web. VLDB Conference, pp 129--138, Italy, 2001.
[7]
Salton, G. and Buckely, C. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41 (4): pp. 288--297, 1990.
[8]
Salton, G., Fox, E. A., and Voorhees. Advanced Feedback Methods in Information Retrieval. Journal of the American Society for Information Science, 36(3): pp. 200--210, 1985.
[9]
Shuang Liu, Fang Liu, Clement Yu, and Weiyi Meng. An Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases. Proceedings of the 27th Annual International ACM SIGIR Conference, pp.266--272, Sheffield, UK, July 2004.
[10]
Zhu, S., Deng, X., Chen, K., and Zheng, W. Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine. Proceedings of Second International Conference in Web Information Systems Engineering, 2001.
[11]
Zonghuan Wu, Vijay Raghavan, Weiyi Meng, Hai He, Clement Yu, and Chun Du. Creating Customized Metasearch Engines on Demand Using SE-LEGO. In Proceedings of Fourth International Conference on Web-Age Information Management (WAIM'03). Demo paper, pp.503--505, Chengdu, China, August 2003.
[12]
http://www.selego.com, Creating MetaSearch Engines On-Demand.

Cited By

View all
  • (2008)An Architectural Framework of a Crawler for Locating Deep Web Repositories Using Learning Multi-agent SystemsProceedings of the 2008 Third International Conference on Internet and Web Applications and Services10.1109/ICIW.2008.94(558-562)Online publication date: 8-Jun-2008

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ACMSE '05 vol 1: Proceedings of the 43rd annual ACM Southeast Conference - Volume 1
March 2005
408 pages
ISBN:1595930590
DOI:10.1145/1167350
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. adaptive
  2. agent
  3. meta-search engine
  4. neural network
  5. relevance feedback
  6. search engine

Qualifiers

  • Article

Conference

ACM SE05
Sponsor:
ACM SE05: ACM Southeast Regional Conference 2005
March 18 - 20, 2005
Georgia, Kennesaw

Acceptance Rates

Overall Acceptance Rate 502 of 1,023 submissions, 49%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2008)An Architectural Framework of a Crawler for Locating Deep Web Repositories Using Learning Multi-agent SystemsProceedings of the 2008 Third International Conference on Internet and Web Applications and Services10.1109/ICIW.2008.94(558-562)Online publication date: 8-Jun-2008

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media