Abstract
Intelligent Web search engines are extremely popular now. Currently, only commercial centralized search engines like Google can process terabytes of Web data. Alternative search engines fulfilling collaborative Web search on a voluntary basis are usually based on a blooming Peer-to-Peer (P2P) technology. In this paper, we investigate the effectiveness of different database selection and result merging methods in the scope of P2P Web search engine Minerva. We adapt existing measures for database selection and results merging, all directly derived from popular document ranking measures, to address the specific issues of P2P Web search. We propose a general approach to both tasks based on the combination of pseudo-relevance feedback methods. From experiments with TREC Web data, we observe that pseudo-relevance feedback improves quality of distributed information retrieval.
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© 2007 Springer Berlin Heidelberg
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Chernov, S., Serdyukov, P., Bender, M., Michel, S., Weikum, G., Zimmer, C. (2007). Database Selection and Result Merging in P2P Web Search. In: Moro, G., Bergamaschi, S., Joseph, S., Morin, JH., Ouksel, A.M. (eds) Databases, Information Systems, and Peer-to-Peer Computing. DBISP2P DBISP2P 2006 2005. Lecture Notes in Computer Science, vol 4125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71661-7_3
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DOI: https://doi.org/10.1007/978-3-540-71661-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71660-0
Online ISBN: 978-3-540-71661-7
eBook Packages: Computer ScienceComputer Science (R0)