An Unsupervised Model For Exploring Hierarchical Semantics From Social Annotations
ISWC'07/ASWC'07: Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference(2007)
摘要
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.
更多查看译文
关键词
social annotation,hierarchical semantics,Semantic Web,social annotation service,social bookmark service,derive hierarchical semantics,hierarchical information,unsupervised model,paper deal,abundant meta-data resource
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络