Abstract
We proposed a framework to construct a semantic-based social network of academic researchers to discover hidden social relationships among the researchers in a particular domain. The challenging task in in the process is to detect accurate relationships that exist among researchers according to their expertise and academic experience. In this paper, we first construct content-based profiles of researchers by crawling online resources. Then background knowledge derived from Wikipedia ,represented in a semantic kernel, is employed to enrich the researchers’ profiles. Researchers’ social network is then constructed based on the similarities among semantic-based profiles. Social communities are then detected by applying the social network analysis and using factors such as experience, background, knowledge level, personal preferences. Representative members of a community are identified using the eigenvector centrality measure. An interesting application of the constructed social network in academic conferences, when there is a need to assign papers to relevant researchers for the review process, is investigated.
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Davoodi, E., Kianmehr, K. (2012). A Semantic-Based Social Network of Academic Researchers. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_34
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DOI: https://doi.org/10.1007/978-3-642-31087-4_34
Publisher Name: Springer, Berlin, Heidelberg
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