Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43205
Title: Web taxonomy integration through co-bootstrapping
Authors: Zhang, D. 
Sun Lee, W. 
Keywords: Boosting
Bootstrapping
Classification
Machine Learning
Ontology Mapping
Semantic Web
Taxonomy Integration
Issue Date: 2004
Source: Zhang, D.,Sun Lee, W. (2004). Web taxonomy integration through co-bootstrapping. Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval : 410-417. ScholarBank@NUS Repository.
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, cobootstrapping, 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.
Source Title: Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/43205
ISBN: 1581138814
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

55
checked on Dec 16, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.