Bisociative Knowledge Discovery pp 1-10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)

Towards Bisociative Knowledge Discovery

  • Michael R. Berthold

Abstract

Knowledge discovery generally focuses on finding patterns within a reasonably well connected domain of interest. In this article we outline a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way. We motivate this approach, show the difference to classical data analysis and conclude by describing a number of different types of domain-crossing connections.

References

  1. 1.
    Berthold, M.R.: Bisociative Knowledge Discovery. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 1–7. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Berthold, M.R. (ed.): Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    Berthold, M.R., Dill, F., Kötter, T., Thiel, K.: Supporting Creativity: Towards Associative Discovery of New Insights. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 14–25. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Boden, M.A.: Précis of the creative mind: Myths and mechanisms. Behavioural and Brain Sciences 17, 519–570 (1994)CrossRefGoogle Scholar
  5. 5.
    Borgelt, C.: Network Creation: Overview. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 51–53. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Dries, A., Nijssen, S., De Raedt, L.: BiQL: A Query Language for Analyzing Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 147–165. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Dubitzky, W., Kötter, T., Schmidt, O., Berthold, M.R.: Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 11–32. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Eronen, L., Toivonen, H.: Biomine: Predicting links between biological entities using network models of heterogeneous database. BMC Bioinformatics ( accepted for publication, 2012)Google Scholar
  9. 9.
    Gossen, T., Nitsche, M., Haun, S., Nürnberger, A.: Data Exploration for Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 287–300. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Haun, S., Gossen, T., Nürnberger, A., Kötter, T., Thiel, K., Berthold, M.R.: On the Integration of Graph Exploration and Data Analysis: The Creative Exploration Toolkit. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 301–312. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., Faloutsos, C.: It’s who you know: graph mining using recursive structural features. In: KDD, pp. 663–671 (2011)Google Scholar
  12. 12.
    Hynönen, T., Mahler, S., Toivonen, H.: Discovery of Novel Term Associations in a Document Collection. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 91–103. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Koestler, A.: The Act of Creation. Macmillan (1964)Google Scholar
  14. 14.
    Kötter, T., Berthold, M.R.: From Information Networks to Bisociative Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 33–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Kötter, T., Berthold, M.R. (Missing) Concept Discovery in Heterogeneous Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 230–245. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Mozetič, I., Lavrač, N.: Applications and Evaluation: Overview. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 359–363. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Nagel, U., Thiel, K., Kötter, T., Piatek, D., Berthold, M.R.: Towards Discovery of Subgraph Bisociations. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 263–284. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Nürnberger, A.: Exploration: Overview. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 285–286. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Rüping, S., Scheffer, T. (eds.): Learning with Multiple Views. Processings of the ICML 2005 Workshop (2005)Google Scholar
  20. 20.
    Segond, M., Borgelt, C.: Cover Similarity Based Item Set Mining. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 104–121. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Sluban, B., Juršič, M., Cestnik, B., Lavrač, N.: Exploring the Power of Outliers for Cross-domain Literature Mining. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 325–337. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Thiel, K., Berthold, M.R.: Node Similarities from Spreading Activation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 246–262. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Toivonen, H.: Network Analysis: Overview. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 144–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Wiswedel, B., Hoeppner, F., Berthold, M.R.: Learning in parallel universes. Data Mining and Knowledge Discovery 21, 130–150 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Michael R. Berthold
    • 1
  1. 1.Nycomed Chair for Bioinformatics and Information Mining, Department of Computer and Information ScienceUniversity of KonstanzGermany

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