Qualitative Graphical Inference with Enhanced Knowledge Fusion

  • Zehua Zhang
  • Duoqian Miao
  • Jin Qian
  • Lei Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


Qualitative reasoning has been widely applied in the analysis of complex networks, but the loss of quantitative information easily result in reasoning conflict. In this article, we adopt a hierarchical decomposition strategy of the network, and present a general framework of qualitative graphical inference based on enhanced multi-level knowledge fusion so as to avoid reasoning conflict. The framework can effectively obtain local knowledge from multilevel analysis and experts experience. Then the method combines the local experience with global structural features, as much as possible to eliminate information loss brought by structural decomposition. Finally, the case also illustrates that the method can effectively solve the trade-off problems in complex networks.


Qualitative graphical inference hierarchical decomposition knowledge fusion 


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  1. 1.
    Wellman, M.P.: Fundamental Concepts of Qualitative Probabilistic Networks. Artificial Intelligence 44, 257–303 (1990)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Renooij, S., van der Gaag, L.C., Parsons, S.: Context-specific Sign-propagation in Qualitative Probabilistic Networks. Artificial Intelligence 144(1), 207–230 (2002)CrossRefGoogle Scholar
  3. 3.
    Renooij, S., van der Gaag, L.C.: Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial Intelligence 172(12-13), 1470–1494 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Van Kouwen, F.A., Renooij, S., Schot, P.: Inference in Qualitative Probabilistic Networks revisited. International Journal of Approximate Reasoning 50(5), 708–720 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Xie, X., Geng, Z., Zhao, Q.: Decomposition of structural learning about directed acyclic graphs. Artificial Intelligence 170, 422–439 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Xiang, Y.: Belief updating in multiply sectioned Bayesian networks without repeated local propagations. International Journal of Approximate Reasoning 23, 1–21 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Wu, D.: Maximal prime subgraph decomposition of Bayesian network: A relational database perspective. International Journal of Approximate Reasoning 46, 334–345 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Liu, W.Y., Yue, K., Gao, M.H.: Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge. Information Sciences 181, 3828–3845 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wang, J., Yao, Y.Y., Wang, F.Y.: Rule + Exception Learning Based on Reduct. Chinese Journal of Computers 28(11), 1778–1789 (2005) (in Chinese)MathSciNetGoogle Scholar
  10. 10.
    Zhang, Z.H., Miao, D.Q., Qian, J.: Hierarchical Qualitative Inference Model with Substructures. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 753–762. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Yao, Y.Y.: Integrative Levels of Granularity. In: Human-Centric Information Processing Through Granular Modeling, pp. 31–47. Springer, Berlin (2009)Google Scholar
  12. 12.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Palo Alto (1988)Google Scholar
  13. 13.
    Koller, D., Friedman, N.: Probabilistic Graphical Models, pp. 45–102. MIT Press, Massachusetts (2009)zbMATHGoogle Scholar
  14. 14.
    Liu, C.L., Wellman, M.P.: Incremental Trade-off Resolution in Qualitative Probabilistic Networks. In: Proc. of Conf. UAI, pp. 338–345 (1998)Google Scholar
  15. 15.
    Yue, K., Liu, W.Y.: Qualitative probabilistic networks with rough-set-based weights. In: Proc. of ICMLC, vol. 3, pp. 1768–1774 (2008)Google Scholar
  16. 16.
    Renooij, S., van der Gaag, L.C.: From qualitative to quantitative probabilistic networks. In: Darwiche, A., Friedman, N. (eds.) Proc. of 18th UAI, pp. 422–429 (2000)Google Scholar
  17. 17.
    Xiang, Y., Hanshar, F.: Tightly and Loosely Coupled Decision Paradigms in Multiagent Expedition. In: Proc. of 4th European Workshop on Probabilistic Graphical Models, pp. 305–312 (2008)Google Scholar
  18. 18.
    Choi, A., Darwiche, A.: A Variational Approach for Approximating Bayesian networks by edge deletion. In: Proc. of 22nd UAI, pp. 80–89 (2006)Google Scholar
  19. 19.
    Lin, T.Y.: Granular computing on binary relations I: data mining and neighborhood systems. In: Proc. of Rough Sets in Knowledge Discovery (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zehua Zhang
    • 1
    • 2
  • Duoqian Miao
    • 1
  • Jin Qian
    • 1
  • Lei Wang
    • 1
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.College of Computer Science and TechnologyTaiyuan University of TechnologyShanxiChina

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