Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been proposed, including metadata extraction, expert profile building, and formal model generation. However, all of them conduct expert search with a coarse-grained approach. With these, further improvements on expert search are hard to achieve. In this paper, we propose conducting expert search with a fine-grained approach. Specifically, we utilize more specific evidences existing in the documents. An evidence-oriented probabilistic model for expert search and a method for the implementation are proposed. Experimental results show that the proposed model and the implementation are highly effective.
['Shenghua Bao', 'Huizhong Duan', 'Qi Zhou', 'Miao Xiong', 'Yunbo Cao', 'Yong Yu']
A Probabilistic Model for Fine-Grained Expert Search