Experts search and rank with social network: An ontology-based approach

Experts finding, one of the most important tasks in social networks, is aimed at identifying individuals with relevant expertise or experience in a given topic. Several approaches have been proposed for finding experts in social networks from documents or web repositories. However, the semantic approach for modeling the information to find experts has not yet been explored. In this paper, we propose a novel method to index the academic information in an ontology-based model for finding and ranking the experts in a particular domain. Additionally, we propose an effective method to construct the academic social network by exploring the relations among the experts and measuring the score of each expert. The score of an expert is measured considering the contributions of relevant publications and relationships among other expert candidates. It is very efficient to find and ranking experts to take advantage of the millions of candidate experts being with relationships. An experiment conducted to evaluate our model shows that experts finding and ranking with an ontological approach integrated with the social network is more effective than other approaches.