Expert Finding System using Latent Effort Ranking in Academic Social Networks

Abstract

The dynamic nature of social network and the influence it has on the provision of immediate solutions to a simple task made their usage prominent and dependable. Whether it is a task of getting a solution to a trivial problem or buying a gadget online or any other task that involves collaborative effort, interacting with people across the globe, the immediate elucidation that comes into anyone’s mind is the social network. Question Answer systems, Feedback systems, Recommender systems, Reviewer Systems are some of the frequently needed applications that are used by people for taking a decision on performing a day to day task. Experts are needed to maintain such systems which will be helpful for the overall development of the web communities. Finding an expert who can do justice for a question involving multiple domain knowledge is a difficult task. This paper deal with an expert finding approach that involves extraction of expertise that is hidden in the profile documents and publications of a researcher who is a member of academic social network. Keywords extracted from an expert’s profile are correlated against index terms of the domain of expertise and the experts are ranked in the respective domains. This approach emphasizes on text mining to retrieve prominent keywords from publications of a researcher to identify his expertise and visualizes the result after statistical analysis.