Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Using sampled data and regression to merge search engine results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Logic and uncertainty in information retrieval
Lectures on information retrieval
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Resource selection and data fusion in multimedia distributed digital libraries
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Distributed query sampling: a quality-conscious approach
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive query-based sampling for distributed IR
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using query logs to establish vocabularies in distributed information retrieval
Information Processing and Management: an International Journal
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Robust result merging using sample-based score estimates
ACM Transactions on Information Systems (TOIS)
Risk-Aware Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
SUSHI: scoring scaled samples for server selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Classification-based resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Predicting the effectiveness of queries and retrieval systems
ACM SIGIR Forum
Foundations and Trends in Information Retrieval
Evaluating server selection for federated search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Distributed information retrieval and applications
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Search result diversification in resource selection for federated search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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The distributed retrieval process is plagued by uncertainty. Sampling, selection, merging and ranking are all based on very limited information compared to centralized retrieval. In this paper, we focus our attention on reducing the uncertainty within the resource selection phase by obtaining a number of estimates, rather than relying upon only one point estimate. We propose three methods for reducing uncertainty which are compared against state-of-the-art baselines across three distributed retrieval testbeds. Our results show that the proposed methods significantly improve baselines, reduce the uncertainty and improve robustness of resource selection.