Prior research in resource selection for federated search mainly focused on selecting a small number of information sources that are most relevant to a user query. However, result novelty and diversification are largely unexplored, which does not reflect the various kinds of information needs of users in real world applications.

Result merging is an important research problem in federated search for merging documents retrieved from multiple ranked lists of selected information sources into a single list. The state-of-the-art result merging algorithms such as Semi-Supervised Learning (SSL) and Sample-Agglomerate Fitting Estimate (SAFE) try to map document scores retrieved from different sources to comparable scores according to a single centralized retrieval algorithm for ranking those documents. Both SSL and SAFE arbitrarily select a single centralized retrieval algorithm for generating comparable document scores, which is problematic in a heterogeneous federated search environment, since a single centralized algorithm is often suboptimal for different information sources. Based on this observation, this paper proposes a novel approach for result merging by utilizing multiple centralized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute comparable document scores. The paper shows that this simple approach generates suboptimal results as it is not flexible enough to deal with heterogeneous information sources. A mixture probabilistic model is thus proposed to learn more appropriate combination weights with respect to different types of information sources with some training data. An extensive set of experiments on three datasets have proven the effectiveness of the proposed new approach.

Result merging is an important step in federated search to merge the documents returned from multiple source-specific ranked lists for a user query. Previous result merging methods such as Semi-Supervised Learning (SSL) and Sample- Agglomerate Fitting Estimate (SAFE) use regression methods to estimate global document scores from document ranks in individual ranked lists. SSL relies on overlapping documents that exist in both individual ranked lists and a centralized sample database. SAFE goes a step further by using both overlapping documents with accurate rank information and documents with estimated rank information for regression. However, existing methods do not distinguish the accurate rank information from the estimated information. Furthermore, all documents are assigned equal weights in regression while intuitively, documents in the top should carry higher weights. This paper proposes a weighted curve fitting method for result merging in federated search. The new method explicitly models the importance of information from overlapping documents over non-overlapping ones. It also weights documents at different positions differently. Empirically results on two datasets clearly demonstrate the advantage of the proposed algorithm.

Resource selection is an important task in Federated Search to select a small number of most relevant information sources. Current resource selection algorithms such as GlOSS, CORI, ReDDE, Geometric Average and the recent classification-based method focus on the evidence of individual information sources to determine the relevance of available sources. Current algorithms do not model the important relationship information among individual sources. For example, an information source tends to be relevant to a user query if it is similar to another source with high probability of being relevant. This paper proposes a joint probabilistic classification model for resource selection. The model estimates the probability of relevance of information sources in a joint manner by considering both the evidence of individual sources and their relationship. An extensive set of experiments have been conducted on several datasets to demonstrate the advantage of the proposed model.