Web taxonomy integration through co-bootstrapping

Zhang, Dell and Lee, W.S. (2004) Web taxonomy integration through co-bootstrapping. In: Sanderson, M. and Jarvelin, K. and Allan, J. and Bruza, P. (eds.) SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp. 410-417. ISBN 9781581138818.

Full text not available from this repository.

Official URL: https://doi.org/10.1145/1008992.1009062

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 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.

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 15:13
Last Modified:	15 Nov 2021 15:13
URI:	https://eprints.bbk.ac.uk/id/eprint/46731

Statistics

Archive Staff Only (login required)

Edit/View Item