中文摘要：

在标注系统的聚会，人们能注解任意的标签到联机数据分类并且索引他们。然而，缺乏词的一个 priori 集合使让人关于标签的语义到达一致困难并且怎么分类数据。本体论基于途径罐头帮助到达如此的一致，而是他们仍然正在面对象模型的无能那样的问题模糊、新的概念适当地。为标签，那被使用很少的时间自从他们能仅仅在很特定的上下文被使用，他们的语义很清楚、详细说明。尽管人们没在这些标签上有一致，为其它建模的语义标注，对这些详细说明了的力量仍然可能。在这份报纸，我们介绍象模型一样的随机的散步和传播激活用不得人心的标签的语义代表标签的语义。由在聚类任务的一个概念把建议模型比作经典潜伏的语义分析途径，我们证明建议模型罐头适当地捕获标签的语义。

英文摘要：

In social tagging systems,people can annotate arbitrary tags to online data to categorize and index them.However,the lack of the ＂a priori＂ set of words makes it difficult for people to reach consensus about the semantics of tags and how to categorize data.Ontologies based approaches can help reaching such consensus,but they are still facing problems such as inability of model ambiguous and new concepts properly.For tags that are used very few times,since they can only be used in very specific contexts,their semantics are very clear and detailed.Although people have no consensus on these tags,it is still possible to leverage these detailed semantics to model the other tags.In this paper we introduce a random walk and spreading activation like model to represent the semantics of tags using semantics of unpopular tags.By comparing the proposed model to the classic Latent Semantic Analysis approach in a concept clustering task,we show that the proposed model can properly capture the semantics of tags.