Personalized resource categorisation in folksonomies

Folksonomies constitute an important type of Web 2.0 services, where users collectively annotate (or \tag") resources to create custom categories. Semantic relation of these categories hint at the possibility of another categorization at a higher level. Discovering these more general categories, called \topics", is an important task. One problem is to discover these semantically coherent topics and the accompanying small sets of tags that cover these topics in order to facilitate more detailed item search. Another important problem is to find words/phrases that describe these topics, i.e. labels or \meta-tag"s. These labeled topics can immensely increase the item search eficiency of users in a folksonomy service. However, this possibility has not been suficiently exploited to date. In this paper, a probabilistic model is used to identify topics in a folksonomy, which are then associated with relevant, descriptive meta-tags. In addition, a small set of diverse and relevant tags are found which cover the semantics of the topic well. The resulting topics form a personalized categorization of folksonomy data due to the personalized nature of the model employed. The results show that the proposed method is successful at discovering important topics and the corresponding identifying meta-tags.