Mining a Web2.0 Service for the Discovery of Semantically Similar Terms: A Case Study with Del.icio.us

Authors:
Kwan Yi
Affiliations:
School of Library and Information Science, University of Kentucky, Lexington, USA 40506
Venue:
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Year:
2008

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Abstract

This study develops and implements methods of identifying similar terms using collaboratively constructed folksonomies. In this study, two folksonomy- based methods are proposed with an aim of demonstrating the usefulness of folksonomy as a source for the discovery of similar terms, especially for `non-in-the-dictionary' terms: co-occurrence-based and correlation-based methods. The experimental results show that the co-occurrence-based method performs comparatively better and that the folksonomies have a potential as a source for the discovery of similar or near-similar terms. The result implies that as the web2.0 service for the folksonomies evolves, the potential of folksonomy for the task will be increased.