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Neural Networks for Relational Data

  • Navdeep KaurEmail author
  • Gautam Kunapuli
  • Saket Joshi
  • Kristian Kersting
  • Sriraam Natarajan
Conference paper
  • 66 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11770)

Abstract

While deep networks have been enormously successful, they rely on flat-feature vector representations. Using them in structured domains requires significant feature engineering. Such domains rely on relational representations to capture complex relationships between entities and their attributes. Thus, we consider the problem of learning neural networks for relational data. We distinguish ourselves from current approaches that rely on expert hand-coded rules by learning higher-order random-walk features to capture local structural interactions and the resulting network architecture. We further exploit parameter tying, where instances of the same rule share parameters. Our experimental results demonstrate the effectiveness of the proposed approach over multiple neural net baselines as well as state-of-the-art relational models.

Keywords

Neural networks Relational models 

Notes

Acknowledgements

SN, GK & NK gratefully acknowledge AFOSR award FA9550-18-1-0462. The authors acknowledge the support of Amazon faculty award. KK acknowledges the support of the RMU project DeCoDeML. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the AFOSR, Amazon, DeCoDeML or the US government.

References

  1. 1.
    De Raedt, L., Kersting, K., Natarajan, S., Poole, D.: Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Morgan & Claypool (2016)Google Scholar
  2. 2.
    França, M.V.M., Zaverucha, G., d’Avila Garcez, A.S.: Fast relational learning using bottom clause propositionalization with artificial neural networks. MLJ (2014)Google Scholar
  3. 3.
    Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)CrossRefGoogle Scholar
  4. 4.
    Jaeger, M.: Parameter learning for relational bayesian networks. In: ICML (2007)Google Scholar
  5. 5.
    Kaur, N., Kunapuli, G., Khot, T., Kersting, K., Cohen, W., Natarajan, S.: Relational restricted Boltzmann machines: a probabilistic logic learning approach. In: ILP (2017)Google Scholar
  6. 6.
    Kazemi, S.M., Buchman, D., Kersting, K., Natarajan, S., Poole, D.: Relational logistic regression. In: KR (2014)Google Scholar
  7. 7.
    Kazemi, S.M., Poole, D.: RelNN: a deep neural model for relational learning. In: AAAI (2018)Google Scholar
  8. 8.
    Khot, T., Natarajan, S., Kersting, K., Shavlik, J.: Learning Markov logic networks via functional gradient boosting. In: ICDM (2011)Google Scholar
  9. 9.
    Lao, N., Cohen, W.: Relational retrieval using a combination of path-constrained random walks. In: JMLR (2010)Google Scholar
  10. 10.
    Lavrac, N., Džeroski, V.: Inductive Logic Programming: Techniques and Applications. Prentice Hall, New Jersey (1993)zbMATHGoogle Scholar
  11. 11.
    Lodhi, H.: Deep relational machines. In: ICONIP (2013)Google Scholar
  12. 12.
    Muggleton, S.: Inverse entailment and Progol. New Generation Computing (1995)Google Scholar
  13. 13.
    Natarajan, S., Khot, T., Kersting, K., Guttmann, B., Shavlik, J.: Gradient-based boosting for statistical relational learning: relational dependency network case. MLJ (2012)Google Scholar
  14. 14.
    Natarajan, S., Tadepalli, P., Dietterich, T.G., Fern, A.: Learning first-order probabilistic models with combining rules. ANN MATH ARTIF INTEL (2008)Google Scholar
  15. 15.
    Šourek, G., Aschenbrenner, V., Železny, F., Kuželka, O.: Lifted relational neural networks. In: NeurIPS Workshop (2015)Google Scholar
  16. 16.
    Šourek, G., Svatoš, M., Železný, F., Schockaert, S., Kuželka, O.: Stacked structure learning for lifted relational neural networks. In: ILP (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Navdeep Kaur
    • 1
    Email author
  • Gautam Kunapuli
    • 1
  • Saket Joshi
    • 2
  • Kristian Kersting
    • 3
  • Sriraam Natarajan
    • 1
  1. 1.The University of Texas at DallasRichardsonUSA
  2. 2.Amazon Inc.SeattleUSA
  3. 3.TU DarmstadtDarmstadtGermany

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