Web taxonomy integration using support vector machines

Zhang, Dell and Lee, W.S. (2004) Web taxonomy integration using support vector machines. In: Feldman, S.I. and Uretsky, M. and Najork, M and Wills, C.E. (eds.) WWW 2004: Proceedings of the 13th international conference on World Wide Web. ACM, pp. 472-481. ISBN 9781581138443.

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Official URL: https://doi.org/10.1145/988672.988736

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. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. 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. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	15 Nov 2021 15:37
Last Modified:	09 Aug 2023 12:52
URI:	https://eprints.bbk.ac.uk/id/eprint/46733

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