A Hybrid Semantic Approach to Building Dynamic Maps of Research Communities
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In earlier papers we characterised the notion of diachronic topic-based communities –i.e., communities of people who work on semantically related topics at the same time. These communities are important to enable topic-centred analyses of the dynamics of the research world. In this paper we present an innovative algorithm, called Research Communities Map Builder (RCMB), which is able to automatically link diachronic topic-based communities over subsequent time intervals to identify significant events. These include topic shifts within a research community; the appearance and fading of a community; communities splitting, merging, spawning other communities; and others. The output of our algorithm is a map of research communities, annotated with the detected events, which provides a concise visual representation of the dynamics of a research area. In contrast with existing approaches, RCMB enables a much more fine-grained understanding of the evolution of research communities, with respect to both the granularity of the events and the granularity of the topics. This improved understanding can, for example, inform the research strategies of funders and researchers alike. We illustrate our approach with two case studies, highlighting the main communities and events that characterized the World Wide Web and Semantic Web areas in the 2000 – 2010 decade.
KeywordsSemantic Web Community Detection Change Detection Trend Detection Pattern Recognition Data Mining Scholarly Data
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- 9.Osborne, F., Scavo, G., Motta, E.: Identifying diachronic topic-based research communities by clustering shared research trajectories. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 114–129. Springer, Heidelberg (2014)Google Scholar
- 10.Osborne, F., Motta, E., Mulholland, P.: Exploring Scholarly Data with Rexplore. In: Proceedings of the 12th International Semantic Web Conference (2013)Google Scholar
- 14.Hofmann, T.: Probabilistic latent semantic indexing. In: The 22nd Conference on Research and Development in Information Retrieval (pp, Berkeley, CA, pp. 50–57 (1999)Google Scholar
- 16.Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceeding of KDD 2008, pp. 990–998 (2008)Google Scholar
- 17.Peroni, S., Shotton, D.: FaBiO and CiTO: ontologies for describing bibliographic resources and citations. In: Web Semantics: Science, Services and Agents on the WWW, vol. 17 (2012)Google Scholar
- 20.Neill, D.B., Moore, A.W., Sabhnani, M., Daniel, K.: Detection of emerging space-time clusters. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 218–227. ACM (2005)Google Scholar
- 21.Sethi, I.K., Patel, N.V.: Statistical approach to scene change detection. In: Symposium on Electronic Imaging: Science & Technology. SPIE (1995)Google Scholar
- 22.Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2(3), 267–278 (1994)Google Scholar
- 24.Hendler, J.: Where are all the Intelligent Agents? A Letter from the Editor in Intelligent Systems IEEE (May/June 2007)Google Scholar