Bisociative Discovery of Interesting Relations between Domains

  • Uwe Nagel
  • Kilian Thiel
  • Tobias Kötter
  • Dawid Piątek
  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)


The discovery of surprising relations in large, heterogeneous information repositories is gaining increasing importance in real world data analysis. If these repositories come from diverse origins, forming different domains, domain bridging associations between otherwise weakly connected domains can provide insights into the data that can otherwise not be accomplished. In this paper, we propose a first formalization for the detection of such potentially interesting, domain-crossing relations based purely on structural properties of a relational knowledge description.


Connected Domain Structural Hole Interesting Relation Activation Vector Information Unit 
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.
    Berthold, M.R., Brandes, U., Kötter, T., Mader, M., Nagel, U., Thiel, K.: Pure spreading activation is pointless. In: Proceedings of the CIKM the 18th Conference on Information and Knowledge Management, pp. 1915–1919 (2009)Google Scholar
  2. 2.
    Boden, M.A.: Précis of the creative mind: Myths and mechanisms. Behavioral and Brain Sciences 17(03), 519–531 (1994)CrossRefGoogle Scholar
  3. 3.
    Burt, R.S.: Structural holes: the social structure of competition. Harvard University Press, Cambridge (1992)Google Scholar
  4. 4.
    Cook, D.J., Holder, L.B.: Mining graph data. Wiley Interscience, Hoboken (2007)zbMATHGoogle Scholar
  5. 5.
    Ford, N.: Information retrieval and creativity: Towards support for the original thinker. Journal of Documentation 55(5), 528–542 (1999)CrossRefGoogle Scholar
  6. 6.
    Freeman, L.C.: A set of measures of centrality based upon betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  7. 7.
    Koestler, A.: The Act of Creation. Macmillan, Basingstoke (1964)Google Scholar
  8. 8.
    Kötter, T., Thiel, K., Berthold, M.R.: Domain bridging associations support creativity. In: Proceedings of the International Conference on Computational Creativity, Lisbon, pp. 200–204 (2010)Google Scholar
  9. 9.
    Maxwell, J.C.: A treatise on electricity and magnetism. Nature 7, 478–480 (1873)CrossRefzbMATHGoogle Scholar
  10. 10.
    Onuma, K., Tong, H., Faloutsos, C.: Tangent: a novel, ’surprise me’, recommendation algorithm. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 657–666 (2009)Google Scholar
  11. 11.
    Poincaré, H.: Mathematical creation. Resonance 5(2), 85–94 (2000)CrossRefGoogle Scholar
  12. 12.
    Thiel, K., Berthold, M.R.: Node similarities from spreading activation. In: Proceedings of the IEEE International Conference on Data Mining, pp. 1085–1090 (2010)Google Scholar
  13. 13.
    Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Uwe Nagel
    • 1
  • Kilian Thiel
    • 1
  • Tobias Kötter
    • 1
  • Dawid Piątek
    • 1
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information Mining Dept. of Computer and Information ScienceUniversity of KonstanzGermany

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