Advertisement

Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks

  • Ji ZhangEmail author
  • Leonard Tan
  • Xiaohui Tao
  • Dianwei Wang
  • Josh Jia-Ching Ying
  • Xin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Knowledge Graphs (KGs) have deep and impactful applications in a wide-array of information networks such as natural language processing, recommendation systems, predictive analysis, recognition, classification, etc. Embedding real-life relational representations in KGs is an essential process of abstracting facts for many important data mining tasks like information retrieval, privacy and control, enrichment and so on. In this paper, we investigate the embedding of the relational fractals which are learned from the Relational Turbulence profiles in the transactions of Online Social Networks (OSNs) into KGs. These relational fractals have the capability of building both compositional-depth hierarchies and shallow-wide continuous vector spaces for more efficient computations on devices with limited resources. The results from our RFT model show accurate predictions of relational turbulence patterns in OSNs which can be used to evolve facts in KGs for more accurate and timely information representations.

Keywords

Relational turbulence Deep learning Knowledge graph embedding Online Social Networks Fact evolution 

References

  1. 1.
    Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)CrossRefGoogle Scholar
  2. 2.
    Cai, L., Wang, W.Y.: Kbgan: Adversarial learning for knowledge graph embeddings. arXiv preprint arXiv:1711.04071 (2017)
  3. 3.
    Deng, L., Yu, D., et al.: Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gideon, J., Khorram, S., Aldeneh, Z., Dimitriadis, D., Provost, E.M.: Progressive neural networks for transfer learning in emotion recognition. arXiv preprint arXiv:1706.03256 (2017)
  5. 5.
    Goh, K.I., Salvi, G., Kahng, B., Kim, D.: Skeleton and fractal scaling in complex networks. Phys. Rev. Lett. 96(1), 018701 (2006)CrossRefGoogle Scholar
  6. 6.
    Haunani Solomon, D., Theiss, J.: A longitudinal test of the relational turbulence model of romantic relationship development. Pers. Relat. 15, 339–357 (2008).  https://doi.org/10.1111/j.1475-6811.2008.00202.xCrossRefGoogle Scholar
  7. 7.
    Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
  8. 8.
    Keshmiri, S., Sumioka, H., Nakanishi, J., Ishiguro, H.: Emotional state estimation using a modified gradient-based neural architecture with weighted estimates. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4371–4378. IEEE (2017)Google Scholar
  9. 9.
    Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648 (2016)
  10. 10.
    Li, X., Lou, C., Zhao, J., Wei, H., Zhao, H.: “tom” pet robot applied to urban autism. arXiv preprint arXiv:1905.05652 (2019)
  11. 11.
    Liu, S., Trenkler, G.: Hadamard, khatri-rao, kronecker and other matrix products. Int. J. Inform. Syst. Sci. 4(1), 160–177 (2008)MathSciNetzbMATHGoogle Scholar
  12. 12.
    McLaren, R.M., Solomon, D.H., Priem, J.S.: The effect of relationship characteristics and relational communication on experiences of hurt from romantic partners. J. Commun. 62(6), 950–971 (2012)CrossRefGoogle Scholar
  13. 13.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2015)CrossRefGoogle Scholar
  14. 14.
    Ramanathan, V., Yao, B., Fei-Fei, L.: Social role discovery in human events. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2475–2482 (2013)Google Scholar
  15. 15.
    Solomon, D.H., Knobloch, L.K.: Relationship uncertainty, partner interference, and intimacy within dating relationships. J. Soc. Pers. Relat. 18(6), 804–820 (2001).  https://doi.org/10.1177/0265407501186004CrossRefGoogle Scholar
  16. 16.
    Solomon, D.H., Knobloch, L.K., Theiss, J.A., McLaren, R.M.: Relational turbulence theory: explaining variation in subjective experiences and communication within romantic relationships. Hum. Commun. Res. 42(4), 507–532 (2016)CrossRefGoogle Scholar
  17. 17.
    Trivedi, R., Dai, H., Wang, Y., Song, L.: Know-evolve: deep temporal reasoning for dynamic knowledge graphs. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3462–3471. JMLR. org (2017)Google Scholar
  18. 18.
    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)CrossRefGoogle Scholar
  19. 19.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)Google Scholar
  20. 20.
    Wilmot, W., et al.: Interpersonal Conflict, 9th edn. McGraw-Hill Higher Education, New York (2007)Google Scholar
  21. 21.
    Zhang, J., Tan, L., Tao, X.: On relational learning and discovery in social networks: a survey. Int. J. Mach. Learn. Cybern. 20(8), 1–18 (2018).  https://doi.org/10.1007/s13042-018-0823-8CrossRefGoogle Scholar
  22. 22.
    Zhang, J., et al.: Detecting relational states in online social networks. In: 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), pp. 38–43. IEEE (2018)Google Scholar
  23. 23.
    Zhang, J., Tan, L., Tao, X., Zheng, X., Luo, Y., Lin, J.C.-W.: SLIND: identifying stable links in online social networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 813–816. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91458-9_54CrossRefGoogle Scholar
  24. 24.
    Zhang, J., Tao, X., Tan, L., Lin, J.C.-W., Li, H., Chang, L.: On link stability detection for online social networks. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11029, pp. 320–335. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-98809-2_20CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ji Zhang
    • 1
    • 2
    Email author
  • Leonard Tan
    • 1
  • Xiaohui Tao
    • 1
  • Dianwei Wang
    • 3
  • Josh Jia-Ching Ying
    • 4
  • Xin Wang
    • 5
  1. 1.University of Southern QueenslandToowoombaAustralia
  2. 2.Zhejiang LabZhejiangChina
  3. 3.Xi’an University of Posts and TelecommunicationsXi’anChina
  4. 4.National Chung Hsing UniversityTaichungTaiwan ROC
  5. 5.Southwest Jiaotong UniversityChengduChina

Personalised recommendations