Patterns and Logic for Reasoning with Networks

  • Angelika Kimmig
  • Esther Galbrun
  • Hannu Toivonen
  • Luc De Raedt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)


Biomine and ProbLog are two frameworks to implement bisociative information networks (BisoNets). They combine structured data representations with probabilities expressing uncertainty. While Biomine is based on graphs, ProbLog’s core language is that of the logic programming language Prolog. This chapter provides an overview of important concepts, terminology, and reasoning tasks addressed in the two systems. It does so in an informal way, focusing on intuition rather than on mathematical definitions. It aims at bridging the gap between network representations and logical ones.


Answer Enumeration Reasoning Task Probabilistic Graph Query Node Prolog Program 
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.


  1. 1.
    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
  2. 2.
    Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link Discovery in Graphs Derived from Biological Databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35–49. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discovery. In: Veloso, M.M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2462–2467 (2007)Google Scholar
  4. 4.
    Kimmig, A., Van den Broeck, G., De Raedt, L.: An algebraic Prolog for reasoning about possible worlds. In: Burgard, W., Roth, D. (eds.) Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011), pp. 209–214. AAAI Press (2011)Google Scholar
  5. 5.
    Flach, P.: Simply logical - Intelligent Reasoning by Example. John Wiley (1994),
  6. 6.
    De Raedt, L.: Logical and Relational Learning. Springer (2008)Google Scholar
  7. 7.
    Muggleton, S.: Duce, an oracle-based approach to constructive induction. In: McDermott, J.P. (ed.) Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI 1987), pp. 287–292 (1987)Google Scholar
  8. 8.
    Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. J. Artif. Intell. Res. (JAIR) 1, 231–255 (1994)Google Scholar
  9. 9.
    Kimmig, A., Demoen, B., De Raedt, L., Santos Costa, V., Rocha, R.: On the implementation of the probabilistic logic programming language ProbLog. Theory and Practice of Logic Programming (TPLP) 11(2-3), 235–262 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Langohr, L., Toivonen, H.: Finding representative nodes in probabilistic graphs. In: Proceedings of the Workshop on Explorative Analytics of Information Networks at ECML PKDD, WEAIN 2009 (2009)Google Scholar
  11. 11.
    Kimmig, A., De Raedt, L.: Local query mining in a probabilistic Prolog. In: Boutilier, C. (ed.) Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 1095–1100 (2009)Google Scholar
  12. 12.
    De Raedt, L., Thon, I.: Probabilistic Rule Learning. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS, vol. 6489, pp. 47–58. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Kimmig, A., De Raedt, L., Toivonen, H.: Probabilistic Explanation Based Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 176–187. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    De Raedt, L., Kersting, K., Torge, S.: Towards learning stochastic logic programs from proof-banks. In: Veloso, M.M., Kambhampati, S. (eds.) Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), pp. 752–757. AAAI Press/The MIT Press (2005)Google Scholar
  15. 15.
    Toivonen, H., Mahler, S., Zhou, F.: A Framework for Path-Oriented Network Simplification. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 220–231. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Zhou, F., Mahler, S., Toivonen, H.: Network simplification with minimal loss of connectivity. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.): Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), pp. 659–668 (2010)Google Scholar
  17. 17.
    Hintsanen, P.: The Most Reliable Subgraph Problem. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 471–478. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    De Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., Toivonen, H.: Compressing probabilistic Prolog programs. Machine Learning 70(2-3), 151–168 (2008)CrossRefzbMATHGoogle Scholar
  19. 19.
    Hintsanen, P., Toivonen, H.: Finding reliable subgraphs from large probabilistic graphs. Data Mining and Knowledge Discovery 17(1), 3–23 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kasari, M., Toivonen, H., Hintsanen, P.: Fast Discovery of Reliable k-terminal Subgraphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part II. LNCS, vol. 6119, pp. 168–177. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Angelika Kimmig
    • 1
  • Esther Galbrun
    • 2
  • Hannu Toivonen
    • 2
  • Luc De Raedt
    • 1
  1. 1.Departement ComputerwetenschappenK.U. LeuvenHeverleeBelgium
  2. 2.Department of Computer Science and Helsinki Institute for Information Technology HIITUniversity of HelsinkiFinland

Personalised recommendations