Constructing and mining a semantic-based academic social network

A number of studies have focused on how to construct an academic social network. The relational ties among researchers are either co-authorship or shared keywords that are captured from scientific journals. The problem with such a network is that researchers are limited within their professional social network. In this paper, we propose a novel method for building a social network explicitly based on researchers' knowledge interests. The researcher's profile is automatically generated from metadata of scientific publications and homepage. By measuring the similarity between topics of interest, we are able to construct a researcher social network with relational linkages among authors that are produced by matching the their corresponding profiles. A direct loop graph-based social network is proposed. The graph naturally represents such a social network. Interestingly, our results showed that a social network based on profile matching is more representative than network based on publication co-authorship or shared keywords. Researcher mining in the academic social network has been explored via two problems Researcher Ranking and Expert Finding.