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An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations
| Citations: | 26 - 0 self |
BibTeX
@MISC{Zhou_anunsupervised,
author = {Mianwei Zhou and Shenghua Bao and Xian Wu and Yong Yu},
title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
year = {}
}
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Abstract
Abstract. This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimental results demonstrate our model’s efficiency. 1
Keyphrases
social annotation hierarchical semantics unsupervised model semantic web hierarchical information web resource model applicability social annotation service model efficiency derived hierarchical semantics apparent limitation data set different environment abundant meta-data resource derive hierarchical semantics experimental result synonym phenomenon social bookmark service del
