Abstract

This paper describes and evaluates a retrieval model that considers the problem of data fusion and collection fusion as two faces of the same coin. To establish a clear theoretical foundation for combining various sources of evidence provided either by different search schemes (data fusion) or by distributed information services (collection fusion), we have implemented a retrieval model based on the logistic regression methodology. Participation: Category B, ad-hoc automatic Introduction There exist many reasons for considering multiple sources of evidence in information retrieval (Katzer et al., 1982), (Saracevic & Kantor, 1988), (Harman, 1995), and their integration is usually studied in two distinct contexts. Various retrieval strategies or query formulations may operate on the same collection (data fusion problem) (Belkin et al., 1995), (Lee, 1995), subject described in the first part. The second part deals with the collection fusion problem or how distributed information servers may collaborate to answer to a given request (collection fusion problem) (Callan et al., 1995), (Voorhees et al., 1995). - 1. Data Fusion Problem To combine different retrieval schemes (or different query formulations), a retrieval engine might first find the retrieved set associated with each search scheme, and then merge them into a single effective ranked list. To define this underlying merging function, we may consider, for each retrieved record, its rank and / or its retrieval status value. However, the retrieval status values obtained by various weighting schemes may not have a range of possible similar values, leading to a more complex combination situation. Section 1.1 outlines our test-collection and some evaluations of individual retrieval schemes based on two distinct query ...

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