Database Selection and Result Merging in P2P Web Search

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