International Conference on User Modeling, Adaptation, and Personalization

UMAP 2015: User Modeling, Adaptation and Personalization pp 357-363 | Cite as

Modelling the User Modelling Community (and Other Communities as Well)

  • Dario De Nart
  • Dante Degl’Innocenti
  • Andrea Pavan
  • Marco Basaldella
  • Carlo Tasso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


Discovering and modelling research communities’ activities is a task that can lead to a more effective scientific process and support the development of new technologies. Journals and conferences already offer an implicit clusterization of researchers and research topics, and social analysis techniques based on co-authorship relations can highlight hidden relationships among researchers, however, little work has been done on the actual content of publications. We claim that a content-based analysis on the full text of accepted papers may lead to a better modelling and understanding of communities’ activities and their emerging trends. In this work we present an extensive case study of research community modelling based upon the analysis of over 450 events and 7000 papers.


Natural Language Processing Semantic Similarity Inverse Document Frequency Probabilistic Latent Semantic Analysis Educational Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Barabsi, A., Jeong, H., Nda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications 311(34), 590–614 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Degl’Innocenti, D., De Nart, D., Tasso, C.: A new multi-lingual knowledge-base approach to keyphrase extraction for the italian language. In: Proc. of the 6th Int.l Conf. on Knowledge Discovery and Information Retrieval. SciTePress (2014)Google Scholar
  3. 3.
    Gangemi, A.: A Comparison of Knowledge Extraction Tools for the Semantic Web. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 351–366. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  4. 4.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proc. of the 22nd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR 1999, pp. 50–57. ACM, New York (1999)Google Scholar
  6. 6.
    Joshi, D., Gatica-Perez, D.: Discovering groups of people in google news. In: Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia, pp. 55–64. ACM (2006)Google Scholar
  7. 7.
    Krafft, D.B., Cappadona, N.A., Caruso, B., Corson-Rikert, J., Devare, M., Lowe, B.J., et al.: Vivo: Enabling national networking of scientists. In: Proceedings of the Web Science Conference 2010, pp. 1310–1313 (2010)Google Scholar
  8. 8.
    Newman, M.: Scientific collaboration networks. network construction and fundamental results. Phys. Rev. E 64, 016131 (2001)CrossRefGoogle Scholar
  9. 9.
    Newman, M.: The structure of scientific collaboration networks. Proc. of the National Academy of Sciences 98(2), 404–409 (2001)Google Scholar
  10. 10.
    Sack, W.: Conversation map: a content-based usenet newsgroup browser. In: From Usenet to CoWebs, pp. 92–109. Springer (2003)Google Scholar
  11. 11.
    Velardi, P., Navigli, R., Cucchiarelli, A., D’Antonio, F.: A new content-based model for social network analysis. In: ICSC, pp. 18–25. IEEE Computer Society (2008)Google Scholar
  12. 12.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dario De Nart
    • 1
  • Dante Degl’Innocenti
    • 1
  • Andrea Pavan
    • 1
  • Marco Basaldella
    • 1
  • Carlo Tasso
    • 1
  1. 1.Artificial Intelligence Lab Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly

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