Database Selection and Result Merging in P2P Web Search
Identifieur interne : 000863 ( Main/Exploration ); précédent : 000862; suivant : 000864Database Selection and Result Merging in P2P Web Search
Auteurs : Sergey Chernov [Allemagne] ; Pavel Serdyukov [Pays-Bas] ; Matthias Bender [Allemagne] ; Sebastian Michel [Allemagne] ; Gerhard Weikum [Allemagne] ; Christian Zimmer [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
English descriptors
- Teeft :
- Algorithm, American music, Baseline method, Best combination, Best peer, Best results, Callan, Database, Database selection, Database selection method, Database selection task, Database systems, Document, Document frequency, Document language models, Document retrieval, Expansion methods, Expansion terms, Feedback, Generation probability, Global english language model, Information retrieval, International conference, Knowledge management cikm, Language model, Language modeling approach, Language modeling framework, Master thesis, Minerva, Minerva system, Model estimation, Modeling, Original papers, Peer, Peer list, Peer lists, Query, Query execution, Query modeling, Query modeling methods, Query term, Relevant documents, Retrieval, Retrieval algorithms, Retrieval quality, Saarland university, Same time, Search engine, Search engines, Selection methods, Similarity scores, Term frequency, Total number.
Abstract
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.
Url:
DOI: 10.1007/978-3-540-71661-7_3
Affiliations:
- Allemagne, Pays-Bas
- Basse-Saxe, Overijssel, Sarre (Land)
- Enschede, Hanovre, Sarrebruck
- Université de Twente
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 001372
- to stream Istex, to step Curation: 001279
- to stream Istex, to step Checkpoint: 000677
- to stream Main, to step Merge: 000863
- to stream Main, to step Curation: 000863
Le document en format XML
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