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A joint probabilistic classification model for resource selection

Published: 19 July 2010 Publication History

Abstract

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.

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 July 2010

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    Author Tags

    1. federated search
    2. joint classification
    3. resource selection

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
    • (2020)Deep Web Selection Based on Entity AssociationProceedings of the 9th International Conference on Computer Engineering and Networks10.1007/978-981-15-3753-0_79(815-825)Online publication date: 1-Jul-2020
    • (2019)Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement LearningThe World Wide Web Conference10.1145/3308558.3313455(1771-1781)Online publication date: 13-May-2019
    • (2019)Review of Deep Web Data Extraction2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002877(1068-1070)Online publication date: Dec-2019
    • (2019)Exploiting Global Impact Ordering for Higher Throughput in Selective SearchAdvances in Information Retrieval10.1007/978-3-030-15719-7_2(12-19)Online publication date: 7-Apr-2019
    • (2017)Aggregated SearchFoundations and Trends in Information Retrieval10.1561/150000005210:5(365-502)Online publication date: 6-Mar-2017
    • (2017)Learning To Rank ResourcesProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080657(837-840)Online publication date: 7-Aug-2017
    • (2017)Enhancing information source selection using a genetic algorithm and social taggingInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2017.07.01137:6(741-749)Online publication date: 1-Dec-2017
    • (2017)LTRo: Learning to Route Queries in Clustered P2P IRAdvances in Information Retrieval10.1007/978-3-319-56608-5_42(513-519)Online publication date: 8-Apr-2017
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