Effective retrieval with distributed collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Approaches to collection selection and results merging for distributed information retrieval
Proceedings of the tenth international conference on Information and knowledge management
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
When one sample is not enough: improving text database selection using shrinkage
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Information source selection for resource constrained environments
ACM SIGMOD Record
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
Consensus-based evaluation framework for distributed information retrieval systems
Knowledge and Information Systems
Semantic overlay networks for p2p systems
AP2PC'04 Proceedings of the Third international conference on Agents and Peer-to-Peer Computing
Improving electronic health records retrieval using contexts
Expert Systems with Applications: An International Journal
An efficient media ports resource discovery for service networks
International Journal of Business Information Systems
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Resource description extracted by query-sampling method can be applied to determine which database sources a certain query should be firstly sent to. In this paper, we propose a contextualized query-sampling method to extract the resources which are most relevant to up-to-date context. Practically, the proposed approach is adopted to personal crawler systems (the so-called focused crawlers), which can support the corresponding user's web navigation tasks in real-time. By taking into account the user context (e.g., intentions or interests), the crawler can build the queries to evaluate candidate information sources. As a result, we can discover semantic associations (i) between user context and the sources, and (ii) between all pairs of the sources. These associations are applied to rank the sources, and transform the queries for the other sources. For evaluating the performance of contextualized query sampling on 53 information sources, we compared the ranking lists recommended by the proposed method with user feedbacks (i.e., ideal ranks), and also computed the precision of discovered subsumptions as semantic associations between the sources.