Providing Advisor Search with Fine Grained Knowledge Sharing

To overcome all problems of not getting relevant information knowledge sharing is the remedy. Knowledge sharing can be done by analyzing knowledge acquired by users. In order to analyze knowledge acquired by web users, analysis of user's web surfing data is very useful. Using web surfing data it is possible to find advisor who most likely possesses the desired piece of fine grained knowledge related with given query. In this dissertation work investigation is done on fine-grained knowledge sharing in collaborative environments. In this work a methodology is proposed to analyze member's web surfing data to summarize the fine-grained knowledge acquired by them and keep a record. A two-step framework is proposed for mining fine-grained knowledge: (1) web surfing data is clustered into tasks by a nonparametric generative model; (2) a novel discriminative infinite Hidden Markov Model (d-iHMM) is developed to mine fine-grained aspects in each task. Finally, the classic expert search method is applied to the mined results to find proper advisor for knowledge sharing. The proposed system is described, and its performance is also evaluated.