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    Web taxonomy integration using spectral graph transducer

    Zhang, Dell and Wang, X. and Dong, Y. (2004) Web taxonomy integration using spectral graph transducer. In: Atzeni, P. and Chu, W.W. and Lu, H. and Zhou, S. and Ling, T.W. (eds.) ER 2004: Conceptual Modelling. Lecture Notes in Computer Science 3288. Springer, pp. 300-312. ISBN 9783540237235.

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    Abstract

    We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. In this paper, we attempt to use a powerful transductive learning algorithm, Spectral Graph Transducer (SGT), to attack this problem. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose to further enhance SGT classifiers by incorporating the affinity information present in the taxonomy data. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 15 Nov 2021 15:06
    Last Modified: 15 Nov 2021 15:06
    URI: https://eprints.bbk.ac.uk/id/eprint/46730

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