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Return to Session IXa: Machine learning for IR
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 learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap. In this paper, we propose a new approach, co-bootstrapping, to enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
@inproceedings{1009062, author = {Dell Zhang and Wee Sun Lee}, title = {Web taxonomy integration through co-bootstrapping}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {410--417}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009062}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
