Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
STARTS: Stanford proposal for Internet meta-searching
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Effective retrieval with distributed collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st 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
A decision-theoretic approach to database selection in networked IR
ACM Transactions on Information Systems (TOIS)
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Server selection on the World Wide Web
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Server Ranking for Distributed Text Retrieval Systems on the Internet
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Unified utility maximization framework for resource selection
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Blog site search using resource selection
Proceedings of the 17th ACM conference on Information and knowledge management
Robust result merging using sample-based score estimates
ACM Transactions on Information Systems (TOIS)
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
Learning from past queries for resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Foundations and Trends in Information Retrieval
Integrating explicit semantic analysis for ontology-based resource selection
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
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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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.