Abstract
Several works in literature investigated the activities of research communities using big data analysis, but the large majority of them focuses on papers and co-authorship relations, ignoring that most of the scientific literature available is already clustered into journals and conferences with a well defined domain of interest. We are interested in bringing out underlying implicit relationships among such containers and more specifically we are focusing on conferences and workshop proceedings available in open access and we exploit a semantic/conceptual analysis of the full free text content of each paper. We claim that such content-based analysis may lead us to a better understanding of the research communities’ activities and their emerging trends. In this work we present a novel method for research communities activity analysis, based on the combination of the results of a Social Network Analysis phase and a Content-Based one. The major innovative contribution of this work is the usage of knowledge-based techniques to meaningfully extract from each of the considered papers the main topics discussed by its authors.
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De Nart, D., Degl’Innocenti, D., Basaldella, M., Agosti, M., Tasso, C. (2016). A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities. In: Calvanese, D., De Nart, D., Tasso, C. (eds) Digital Libraries on the Move. IRCDL 2015. Communications in Computer and Information Science, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-41938-1_15
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