Understanding Research Dynamics

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)


Rexplore leverages novel solutions in data mining, semantic technologies and visual analytics, and provides an innovative environment for exploring and making sense of scholarly data. Rexplore allows users: (1) to detect and make sense of important trends in research; (2) to identify a variety of interesting relations between researchers, beyond the standard co-authorship relations provided by most other systems; (3) to perform fine-grained expert search with respect to detailed multi-dimensional parameters; (4) to detect and characterize the dynamics of interesting communities of researchers, identified on the basis of shared research interests and scientific trajectories; (5) to analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities.


Scholarly data Visual analytics Data exploration Semantic Web Semantic technologies Ontology population Data mining Data Integration 


  1. 1.
    Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006)CrossRefGoogle Scholar
  2. 2.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceeding of the 14th International Conference on Knowledge Discovery and Data Mining, pp. 990–998 (2008)Google Scholar
  3. 3.
    Dunne, C., Shneiderman, B., Gove, R., Klavans, J., Dorr, B.: Rapid understanding of scientific paper collections: integrating statistics, text analytics, and visualization. J. Am. Soc. Inf. Sci. Technol. 63(12), 2351–2369 (2012)CrossRefGoogle Scholar
  4. 4.
    Osborne, F., Motta, E.: Mining semantic relations between research areas. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 410–426. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Osborne, F., Motta, E., Mulholland, P.: Exploring scholarly data with rexplore. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 460–477. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Osborne, F., Motta, E.: Exploring research trends with Rexplore. D-Lib Mag. 19(9/10), 4 (2013)Google Scholar
  7. 7.
    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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

Personalised recommendations