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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 90–105Cite as

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Combining Subjective Probabilities and Data in Training Markov Logic Networks

Combining Subjective Probabilities and Data in Training Markov Logic Networks

  • Tivadar Pápai20,
  • Shalini Ghosh21 &
  • Henry Kautz20 
  • Conference paper
  • 4503 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7523)

Abstract

Markov logic is a rich language that allows one to specify a knowledge base as a set of weighted first-order logic formulas, and to define a probability distribution over truth assignments to ground atoms using this knowledge base. Usually, the weight of a formula cannot be related to the probability of the formula without taking into account the weights of the other formulas. In general, this is not an issue, since the weights are learned from training data. However, in many domains (e.g. healthcare, dependable systems, etc.), only little or no training data may be available, but one has access to a domain expert whose knowledge is available in the form of subjective probabilities. Within the framework of Bayesian statistics, we present a formalism for using a domain expert’s knowledge for weight learning. Our approach defines priors that are different from and more general than previously used Gaussian priors over weights. We show how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge. Additionally, we also provide a formalism for capturing conditional subjective probabilities, which are often easier to obtain and more reliable than non-conditional probabilities. We demonstrate the effectiveness of our approach by extensive experiments in a domain that models failure dependencies in a cyber-physical system. Moreover, we demonstrate the advantages of using our proposed prior over that of using non-zero mean Gaussian priors in a commonly cited social network MLN testbed.

Keywords

  • Training Data
  • Bayesian Network
  • Domain Expert
  • Subjective Probability
  • Exponential Family

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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References

  1. Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers (2009)

    Google Scholar 

  2. Kok, S., Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Wang, J., Nath, A., Domingos, P.: The Alchemy system for statistical relational AI. Technical report, Department of Computer Science and Engineering, University of Washington (2010)

    Google Scholar 

  3. Geiger, D., Meek, C.: Graphical models and exponential families. In: Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 156–165. Morgan Kaufmann, Madison (August 1998)

    Google Scholar 

  4. Fisseler, J.: Toward markov logic with conditional probabilities. In: FLAIRS Conference, pp. 643–648 (2008)

    Google Scholar 

  5. Thimm, M., Kern-Isberner, G., Fisseler, J.: Relational Probabilistic Conditional Reasoning at Maximum Entropy. In: Liu, W. (ed.) ECSQARU 2011. LNCS, vol. 6717, pp. 447–458. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  6. Poon, H., Domingos, P.: Joint unsupervised coreference resolution with markov logic. In: EMNLP, ACL, pp. 650–659 (2008)

    Google Scholar 

  7. Pearl, J.: Probabilistic reasoning in intelligent systems - networks of plausible inference. Morgan Kaufmann series in representation and reasoning. Morgan Kaufmann (1989)

    Google Scholar 

  8. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)

    Google Scholar 

  9. Chung, F.R.K., Mumford, D.: Chordal completions of planar graphs. J. Comb. Theory, Ser. B 62(1), 96–106 (1994)

    CrossRef  MathSciNet  MATH  Google Scholar 

  10. Raiffa, H., Schlaifer, R.: Applied statistical decision theory [by] Howard Raiffa and Robert Schlaifer. In: Division of Research, Division of Research, Graduate School of Business Adminitration, Harvard University, Boston (1961)

    Google Scholar 

  11. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. Springer (2007)

    Google Scholar 

  12. Jain, D., Barthels, A., Beetz, M.: Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters. In: 19th European Conference on Artificial Intelligence (ECAI), pp. 937–942 (2010)

    Google Scholar 

  13. Ghosh, S., Shankar, N., Owre, S.: Machine reading using markov logic networks for collective probabilistic inference. In: Proceedings of ECML-CoLISD 2011 (2011)

    Google Scholar 

  14. Ghosh, S., Shankar, N., Owre, S., David, S., Swan, G., Lincoln, P.: Markov logic networks in health informatics. In: Proceedings of ICML-MLGC 2011 (2011)

    Google Scholar 

  15. Denker, G., Briesemeister, L., Elenius, D., Ghosh, S., Mason, I., Tiwari, A., Bhatt, D., Hailu, H., Madl, G., Nikbin, S., Varadarajan, S., Bauer, G., Steiner, W., Koutsoukos, X., Levendovsky, T.: Probabilistic, compositional, multi-dimension model-based verification (promise)

    Google Scholar 

  16. Poon, H., Domingos, P.: Sound and efficient inference with probabilistic and deterministic dependencies. In: AAAI (2006)

    Google Scholar 

  17. Niculescu, R.S., Mitchell, T.M., Rao, R.B.: Bayesian network learning with parameter constraints. Journal of Machine Learning Research 7, 1357–1383 (2006)

    MathSciNet  MATH  Google Scholar 

  18. Campos, C.P., Tong, Y., Ji, Q.: Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 168–181. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  19. Geman, S., Geman, D.: Readings in computer vision: issues, problems, principles, and paradigms, pp. 564–584. Morgan Kaufmann Publishers Inc., San Francisco (1987)

    Google Scholar 

  20. Li, S.Z.: A markov random field model for object matching under contextual constraints. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 866–869 (1994)

    Google Scholar 

  21. Druck, G., Mann, G., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 595–602. ACM, New York (2008)

    CrossRef  Google Scholar 

  22. Liang, P., Jordan, M.I., Klein, D.: Learning from measurements in exponential families. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 641–648. ACM, New York (2009)

    Google Scholar 

  23. Diecidue, E., Wakker, P., Zeelenberg, M.: Eliciting decision weights by adapting de finetti’s betting-odds method to prospect theory. Open Access publications from Tilburg University urn:nbn:nl:ui:12-225938, Tilburg University (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Rochester, Rochester, NY, USA

    Tivadar Pápai & Henry Kautz

  2. Computer Science Laboratory, SRI International, Menlo Park, CA, USA

    Shalini Ghosh

Authors
  1. Tivadar Pápai
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  2. Shalini Ghosh
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  3. Henry Kautz
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Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach, Tijl De Bie & Nello Cristianini,  & 

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Pápai, T., Ghosh, S., Kautz, H. (2012). Combining Subjective Probabilities and Data in Training Markov Logic Networks. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-33460-3_11

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  • Print ISBN: 978-3-642-33459-7

  • Online ISBN: 978-3-642-33460-3

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