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.
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Yi, K. (2008). Mining a Web2.0 Service for the Discovery of Semantically Similar Terms: A Case Study with Del.icio.us. In: Buchanan, G., Masoodian, M., Cunningham, S.J. (eds) Digital Libraries: Universal and Ubiquitous Access to Information. ICADL 2008. Lecture Notes in Computer Science, vol 5362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89533-6_35
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DOI: https://doi.org/10.1007/978-3-540-89533-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89532-9
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