Intelligent Web search engines are extremely popular now. Currently, only the 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 the general approach to both tasks based on the combination of pseudo-relevance feedback methods. From experiments with TREC Web data, we observe that the pseudo-relevance feedback information from the topically organized collections improves retrieval quality.
|Title of host publication||Proceedings of the 3rd International Workshop on Databases, Information Systems, and Peer-to-Peer Computing (DBISP2P 2005)|
|Place of Publication||Heidelberg, Germany|
|Number of pages||12|
|Publication status||Published - Sep 2005|
|Name||Lecture Notes in Computer Science|
- DB-CAQ: CONTEXT-AWARE QUERYING
Chernov, S., Serdyukov, P., Bender, M., Michel, S., Weikum, G., & Zimmer, C. (2005). Database selection and result merging in P2P web search. In Proceedings of the 3rd International Workshop on Databases, Information Systems, and Peer-to-Peer Computing (DBISP2P 2005) (pp. 26-37). [10.1007/978-3-540-71661-7_3] (Lecture Notes in Computer Science; Vol. 4125). Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-540-71661-7_3