Advertisement

Learning Bayes Nets for Relational Data with Link Uncertainty

  • Zhensong Qian
  • Oliver Schulte
Conference paper
  • 721 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8323)

Abstract

We present an algorithm for learning correlations among link types and node attributes in relational data that represent complex networks. The link correlations are represented in a Bayes net structure. This provides a succinct graphical way to display relational statistical patterns and support powerful probabilistic inferences. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attributes given the existence of links among entities. The models described in this paper capture a wider class of correlations that involve uncertainty about the link structure. Our base line method learns a Bayes net from join tables directly. This is a statistically powerful procedure that finds many correlations, but does not scale well to larger datasets. We compare join table search with a hierarchical search strategy.

Keywords

Descriptive Attribute Markov Logic Network Link Correlation Heterogeneous Information Network Hierarchical Search 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chickering, D.: Optimal structure identification with greedy search. Journal of Machine Learning Research 3, 507–554 (2003)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Morgan and Claypool Publishers (2009)Google Scholar
  3. 3.
    Getoor, L., Friedman, N., Koller, D., Pfeffer, A., Taskar, B.: Probabilistic relational models. In: Introduction to Statistical Relational Learning, ch. 5, pp. 129–173. MIT Press (2007)Google Scholar
  4. 4.
    Getoor, L., Taskar, B., Koller, D.: Selectivity estimation using probabilistic models. ACM SIGMOD Record 30(2), 461–472 (2001)CrossRefGoogle Scholar
  5. 5.
    Heckerman, D., Geiger, D., Chickering, D.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)zbMATHGoogle Scholar
  6. 6.
    Khosravi, H., Man, T., Hu, J., Gao, E., Schulte, O.: Learn and join algorithm code, http://www.cs.sfu.ca/~oschulte/jbn/
  7. 7.
    Khosravi, H., Schulte, O., Man, T., Xu, X., Bina, B.: Structure learning for Markov logic networks with many descriptive attributes. In: AAAI, pp. 487–493 (2010)Google Scholar
  8. 8.
    Khot, T., Natarajan, S., Kersting, K., Shavlik, J.W.: Learning Markov logic networks via functional gradient boosting. In: ICDM, pp. 320–329. IEEE Computer Society (2011)Google Scholar
  9. 9.
    Khot, T., Shavlik, J., Natarajan, S.: Boostr (2013), http://pages.cs.wisc.edu/~tushar/Boostr/
  10. 10.
    Natarajan, S., Khot, T., Kersting, K., Gutmann, B., Shavlik, J.W.: Gradient-based boosting for statistical relational learning: The relational dependency network case. Machine Learning 86(1), 25–56 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Neapolitan, R.E.: Learning Bayesian Networks. Pearson Education (2004)Google Scholar
  12. 12.
    Poole, D.: First-order probabilistic inference. In: IJCAI, pp. 985–991 (2003)Google Scholar
  13. 13.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2010)Google Scholar
  14. 14.
    Schulte, O.: A tractable pseudo-likelihood function for Bayes nets applied to relational data. In: SIAM SDM, pp. 462–473 (2011)Google Scholar
  15. 15.
    Schulte, O.: Challenge paper: Marginal probabilities for instances and classes. In: ICML-SRL Workshop on Statistical Relational Learning (June 2012)Google Scholar
  16. 16.
    Schulte, O., Khosravi, H.: Learning graphical models for relational data via lattice search. Machine Learning 88(3), 331–368 (2012)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Schulte, O., Khosravi, H., Kirkpatrick, A., Gao, T., Zhu, Y.: Modelling relational statistics with bayes nets. In: Inductive Logic Programming, ILP (2012)Google Scholar
  18. 18.
    Schulte, O., Khosravi, H., Kirkpatrick, A., Gao, T., Zhu, Y.: Modelling relational statistics with bayes nets. Machine Learning (2013) (forthcoming)Google Scholar
  19. 19.
    Sun, Y., Han, J.: Mining Heterogeneous Information Networks: Principles and Methodologies, vol. 3. Morgan & Claypool Publishers (2012)Google Scholar
  20. 20.
    The Tetrad Group. The Tetrad project (2008), http://www.phil.cmu.edu/projects/tetrad/
  21. 21.
    Ullman, J.D.: Principles of Database Systems, 2nd edn. W. H. Freeman & Co. (1982)Google Scholar
  22. 22.
    Yin, X., Han, J., Yang, J., Yu, P.S.: Crossmine: Efficient classification across multiple database relations. In: ICDE, pp. 399–410. IEEE Computer Society (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhensong Qian
    • 1
  • Oliver Schulte
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityVancouver-BurnabyCanada

Personalised recommendations