Finding dominating set from verbal contextual graph for personalized search in Folksonomy

With the development of the Internet, user-generated data has been growing tremendously in Web 2.0 era. Facing such a big volume of resources in folksonomy, people need a method of fast exploration and indexing to find their demanded data. To achieve this goal, contextual information is indispensable and valuable to understand user preference and purpose. In sociolinguistics, context can be mainly categorized as verbal context and social context. Comparing with verbal context, social context not only requires domain knowledge to pre-define contextual attributes but also acquires additional data from users. However, there is no research of addressing irrelevant contextual factors for verbal context model so far. The dominating set from verbal context proposed in this paper is to fill this blank. We present the verbal context in folksonomy to capture the user intention, and propose a dominating set discovering method for this verbal context model to prune the irrelevant contextual factors and keep the major characteristics at the same time. Furthermore, the experiments, which are conducted on a public data set, show that the proposed method gives convincing results.