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Identifying diachronic topic-based research communities by clustering shared research trajectories

Osborne, Francesco; Scavo, Beppe and Motta, Enrico (2014). Identifying diachronic topic-based research communities by clustering shared research trajectories. In: Extended Semantic Web Conference 2014 (ESWC 2014) - Research Track.

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Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit ‘static’ relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities –i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world –e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 Springer
Keywords: community detection; scholarly data; scholarly ontologies; semantic technologies; clustering; similarity metrics; fuzzy C-means
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
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Item ID: 39666
Depositing User: Francesco Osborne
Date Deposited: 07 Mar 2014 12:48
Last Modified: 16 Jun 2020 23:10
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