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Savoy, Jacques

Nom
Savoy, Jacques
Affiliation principale
Fonction
Professeur.e ordinaire
Email
jacques.savoy@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/282
_
0000-0002-4486-0067

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  • Publication
    Accès libre
    Fusion de collections dans les métamoteurs
    (2002)
    Savoy, Jacques 
    ;
    Rasolofo, Yves
    ;
    Abbaci, Faïza
    Les métamoteurs disponibles sur le Web offrent la possibilité d'interroger de nombreux serveurs d'information soulevant le problème de la fusion des résultats provenant des différents moteurs interrogés. Dans cet article, nous proposons une nouvelle stratégie de fusion n'utilisant que le rang des documents dépistés par les divers moteurs de recherche consultés. De plus, nous évaluons plusieurs approches en utilisant un corpus de 2 GB comprenant des articles de quotidiens et une seconde collection de pages Web d'environ 10 GB. Basée sur nos expériences, notre stratégie, simple et efficace pour la fusion de collections, présente une performance intéressante et se révèle bien adaptée aux métamoteurs de recherche., We investigate the problem of combining ranked lists of documents provided by multiple search engines. Such a problem must be solved by meta-search engines. In this paper, we suggest a new merging strategy using only the rank of the retrieved items. Moreover, we evaluate various merging approaches based on both a corpus of 2 GB containing news, and a second test-collection of 10 GB of Web pages. Based on our evaluations, our merging approach presents interesting performance and it is well adapted for meta-search engines.
  • Publication
    Accès libre
    Approaches to Collection Selection and Results Merging for Distributed Information Retrieval
    (2001)
    Rasolofo, Yves
    ;
    Abbaci, Faïza
    ;
    Savoy, Jacques 
    We have investigated two major issues in Distributed Information Retrieval (DIR), namely: collection selection and search results merging. While most published works on these two issues are based on pre-stored metadata, the approaches described in this paper involve extracting the required information at the time the query isprocessed. In order to predict the relevance of collections to a given query, we analyse a limited number of full documents (e.g., the top five documents) retrieved from each collection and then consider term proximity within them. On the other hand, our merging technique is rather simple since input only requires document scores and lengths of results lists. Our experiments evaluate the retrieval effectiveness of these approaches and compare them with centralised indexing and various other DIR techniques (e.g., CORI [2][3][23]).
    We conducted our experiments using two testbeds: one containing news articles extracted from four different sources (2 GB) and another containing 10 GB of Web pages. Our evaluations demonstrate that the retrieval effectiveness of our simple approaches is worth considering.