Optimal Advisor Search for Knowledge Sharing in Collaborative Environment

Abstract

In day todays life, many people are accessing the information on the web. In collaborative environments it may be possible that numbers of web users may trying to access the similar information on the web for particular domain. Therefore knowledge sharing in collaborative environment is major research problem for many research communities. For example, in an organization several departments may successively need to buy anti-virus software and employees from these departments may have studied online about different anti-virus software and their features independently. It will be productive to get them connected and share learned knowledge. The domain which is further divided into sub domain is known as micro-aspects in web surfing data. Mining these micro-aspects is critical as it can provide a detailed description of the knowledge gained by a person, which is the basis for advisor search. In this project, We investigate knowledge sharing system in collaborative environments. We propose to analyze members’ Web surfing data to summarize the knowledge acquired by them. A two-step framework is proposed for mining knowledge: (1) Web surfing data is clustered into tasks (Domain)by a k-means algorithm 2) Mine micro aspects (Sub domain)in each task. Finally, search method is applied to the mined results to find proper advisor.