document: Web taxonomy integration using support vector machines 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 .
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