Description

Collaborative environments, which enable company-wide global teams to identify the source of the problem and develop a response, are an excellent antidote to a lack of preparedness. Knowledge sharing is an activity through which knowledge is exchanged among people, friends, families, communities or organizations. In a single domain in order to gain knowledge, user may try to acquire similar information on the Web in this Collaborative environments. In a company there are many employees working in many departments. They need to buy business intelligence software to study. They may have studied in online about different knowledge tools and features independently. It will be secured to get them connected and share their learned knowledge. This project presents the fine-grained knowledge sharing in collaborative environments. This system proposes Web surfing data to analyze the members and to summarize the fine-grained knowledge acquired by them. The framework proposes in this project. That framework has two steps. First, Web surfing data is clustered into tasks by a nonparametric generative model; second, a novel discriminative infinite Hidden Markov Model is developed to mine fine-grained aspects in each task. And to find proper members for knowledge sharing, the classic expert search method is applied to the mined results.