B. Uygar Oztekin
ABSTRACT
Recent increase in the number of search engines on the Web and the availability of meta search engines that can query multiple search engines makes it important to find effective methods for combining results coming from different sources. In this paper we introduce novel methods for reranking in a meta search environment based on expert agreement and contents of the snippets. We also introduce an objective way of evaluating different methods for ranking search results that is based upon implicit user judgements. We incorporated our methods and two variations of commonly used merging methods in our meta search engine, Mearf, and carried out an experimental study using logs accumulated over a period of twelve months. Our experiments show that the choice of the method used for merging the output produced by different search engines plays a significant role in the overall quality of the search results. In almost all cases examined, results produced by some of the new methods introduced were consistently better than the ones produced by traditional methods commonly used in various meta search engines. These observations suggest that the proposed methods can offer a relatively inexpensive way of improving the meta search experience over existing methods.
- Brian T. Bartell, Garrison W. Cottrell, and Richard K. Belew. Automatic combination of multiple ranked retrieval systems. In Research and Development in Information Retrieval, pages 173--181, 1994. Google Scholar
Digital Library
- C4.com. http://www.c4.com/.Google Scholar
- J. P. Callan, Z. Lu, and W. Bruce Croft. Searching Distributed Collections with Inference Networks. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 21--28, Seattle, Washington, 1995. ACM Press. Google ScholarDigital Library
- Daniel Dreilinger and Adele E. Howe. Experiences with selecting search engines using metasearch. ACM Transactions on Information Systems, 15(3):195--222, 1997. Google ScholarDigital Library
- James C. French and Allison L. Powell. Metrics for evaluating database selection techniques. In 10th International Workshop on Database and Expert Systems Applications, 1999. Google ScholarDigital Library
- James C. French, Allison L. Powell, James P. Callan, Charles L. Viles, Travis Emmitt, Kevin J. Prey, and Yun Mou. Comparing the performance of database selection algorithms. In Research and Development in Information Retrieval, pages 238--245, 1999. Google ScholarDigital Library
- E. Glover. Using Extra-Topical User Preferences to Improve Web-Based Metasearch. PhD thesis, 2001. Google ScholarDigital Library
- L. Gravano, H. García-Molina, and A. Tomasic. The effectiveness of GIOSS for the text database discovery problem. SIGMOD Record (ACM Special Interest Group on Management of Data), 23(2):126--137, June 1994. Google ScholarDigital Library
- Adele E. Howe and Daniel Dreilinger. SAVVYSEARCH: A metasearch engine that learns which search engines to query. AI Magazine, 18(2):19--25, 1997.Google ScholarDigital Library
- Inquirus. http://www.inquirus.com/.Google Scholar
- Panagiotis Ipeirotis, Luis Gravano, and Mehran Sahami. Automatic classification of text databases through query probing. Technical Report CUCS-004-00, Computer Science Department, Columbia University, March 2000.Google Scholar
- Ixquick. http://www.ixquick.com/.Google Scholar
- D. D. Lewis. Evaluating and Optimizing Autonomous Text Classification Systems. In E. A. Fox, P. Ingwersen, and R. Fidel, editors, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 246--254, Seattle, Washington, 1995. ACM Press. Google ScholarDigital Library
- Longzhuang Li and Li Shang. Statistical performance evaluation of search engines. In WWW10 conference posters, May 2--5, 2001, Hong Kong.Google Scholar
- Mamma. http://www.mamma.com/.Google Scholar
- M. Catherine McCabe, Abdur Chowdhury, David A. Grossman, and Ophir Frieder. A unified environment for fusion of information retrieval approaches. In ACM-CIKM Conference for Information and Knowledge Management, pages 330--334, 1999. Google ScholarDigital Library
- Metacrawler. http://www.metacrawler.com/.Google Scholar
- Profusion. http://www.profusion.com/.Google Scholar
- E. Selberg. Towards Comprehensive Web Search. PhD thesis, 1999. Google ScholarDigital Library
- E. Selberg and O. Etzioni. Multi-service search and comparison using the MetaCrawler. In Proceedings of the 4th International World-Wide Web Conference, Darmstadt, Germany, December 1995.Google Scholar
- E. Selberg and O. Etzioni. The MetaCrawler architecture for resource aggregation on the Web. IEEE Expert, (January--February):11--14, 1997.Google ScholarCross Ref
- Joseph A. Shaw and Edward A. Fox. Combination of multiple searches. In Third Text REtrieval Conference, 1994.Google Scholar
- Mario Gomez Susan Gauch, Guijun Wang. Profusion: Intelligent fusion from multiple, distributed search engines. Journal of Universal Computer Science, 2(9):637--649, 1996.Google Scholar
- Christopher C. Vogt and Garrison W. Cottrell. Fusion via a linear combination of scores. Information Retrieval, 1(3):151--173, 1999. Google ScholarDigital Library
- Zonghuan Wu, Weiyi Meng, Clement Yu, and Zhuogang Li. Towards a highly-scalable and effective metasearch engine. In WWW10 Conference, May 2--5, 2001, Hong Kong. ACM, 2001. Google ScholarDigital Library
- Clement T. Yu, Weiyi Meng, King-Lup Liu, Wensheng Wu, and Naphtali Rishe. Efficient and effective metasearch for a large number of text databases. In Proceedings of the 1999 ACM CIKM International Conference on Information and Knowledge Management, Kansas City, Missouri, USA, November 2-6, 1999, pages 217--224. ACM, 1999. Google ScholarDigital Library
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