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Event-Enhanced Learning for KG Completion

  • Martin Ringsquandl
  • Evgeny Kharlamov
  • Daria Stepanova
  • Marcel Hildebrandt
  • Steffen Lamparter
  • Raffaello Lepratti
  • Ian Horrocks
  • Peer Kröger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)

Abstract

Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes.

Notes

Acknowledgements

This work was partially supported by the EPSRC projects DBOnto,MaSI\(^3\) and ED\(^3\).

References

  1. 1.
    Abadi, M., Barham, P., Chen, J., Chen, Z., et al.: TensorFlow : a system for large-scale machine learning. In: OSDI (2016)Google Scholar
  2. 2.
    Ali, M.I., Gao, F., Mileo, A.: CityBench: a configurable benchmark to evaluate RSP engines using smart city datasets. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 374–389. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25010-6_25CrossRefGoogle Scholar
  3. 3.
    Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: EMNLP, pp. 615–620 (2014)Google Scholar
  4. 4.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  5. 5.
    Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: EMNLP-CoNLL, pp. 708–716 (2007)Google Scholar
  6. 6.
    Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: ACM SIGKDD, pp. 601–610 (2014)Google Scholar
  7. 7.
    Duchi, J.C., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATHGoogle Scholar
  8. 8.
    Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Evaluating entity linking with Wikipedia. Artif. Intell. 194, 130–150 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kharlamov, E., Hovland, D., Skjæveland, M.G., Bilidas, D., Jiménez-Ruiz, E., Xiao, G., Soylu, A., Lanti, D., Rezk, M., Zheleznyakov, D., Giese, M., Lie, H., Ioannidis, Y.E., Kotidis, Y., Koubarakis, M., Waaler, A.: Ontology based data access in Statoil. JWS 44, 3–36 (2017)CrossRefGoogle Scholar
  10. 10.
    Kharlamov, E., Mailis, T., Mehdi, G., Neuenstadt, C., Özçep, Ö.L., Roshchin, M., Solomakhina, N., Soylu, A., Svingos, C., Brandt, S., Giese, M., Ioannidis, Y.E., Lamparter, S., Möller, R., Kotidis, Y., Waaler, A.: Semantic access to streaming and static data at Siemens. JWS 44, 54–74 (2017)CrossRefGoogle Scholar
  11. 11.
    Krompaß, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 640–655. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25007-6_37CrossRefGoogle Scholar
  12. 12.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: Dbpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)Google Scholar
  13. 13.
    Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52–59. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21735-7_7CrossRefGoogle Scholar
  14. 14.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 1–9 (2013)Google Scholar
  15. 15.
    Minervini, P., Fanizzi, N., D’Amato, C., Esposito, F.: Scalable learning of entity and predicate embeddings for knowledge graph completion. In: ICMLA, pp. 162–167 (2015)Google Scholar
  16. 16.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  17. 17.
    Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)Google Scholar
  18. 18.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: ICML (2011)Google Scholar
  19. 19.
    Ringsquandl, M., Lamparter, S., Brandt, S., Hubauer, T., Lepratti, R.: Semantic-guided feature selection for industrial automation systems. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 225–240. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25010-6_13CrossRefGoogle Scholar
  20. 20.
    Ringsquandl, M., Lamparter, S., Kharlamov, E., Lepratti, R., Stepanova, D., Kroeger, P., Horrocks, I.: On event-driven learning of knowledge in smart factories: the case of siemens. In: IEEE Big Data (2017)Google Scholar
  21. 21.
    Santos, H., Dantas, V., Furtado, V., Pinheiro, P., McGuinness, D.L.: From data to city indicators: a knowledge graph for supporting automatic generation of dashboards. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10250, pp. 94–108. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58451-5_7CrossRefGoogle Scholar
  22. 22.
    Shi, B., Weninger, T.: ProjE : embedding projection for knowledge graph completion. In: AAAI 2017, pp. 1–14 (2017)Google Scholar
  23. 23.
    Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: NIPS, pp. 1–10 (2013)Google Scholar
  24. 24.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of WWW, pp. 697–706 (2007)Google Scholar
  25. 25.
    Wang, Q., Wang, B., Guo, L.: Knowledge base completion using embeddings and rules. In: IJCAI, pp. 1859–1866 (2015)Google Scholar
  26. 26.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119 (2014)Google Scholar
  27. 27.
    Wang, Z., Li, J.Z.J.: Text-enhanced representation learning for knowledge graph. In: IJCAI, pp. 1293–1299 (2016)Google Scholar
  28. 28.
    Xiao, H., Huang, M., Meng, L., Zhu, X.: SSP: semantic space projection for knowledge graph embedding with text descriptions. In: AAAI, pp. 1–10 (2017)Google Scholar
  29. 29.
    Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: IJCAI, pp. 2659–2665 (2016)Google Scholar
  30. 30.
    Yang, Z., Tang, J., Cohen, W.W.: Multi-modal bayesian embeddings for learning social knowledge graphs. In: IJCAI, pp. 2287–2293 (2016)Google Scholar
  31. 31.
    Zhong, H., Zhang, J., Wang, Z., Wan, H., Chen, Z.: Aligning knowledge and text embeddings by entity descriptions. In: EMNLP, pp. 267–272 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ludwig-Maximilians UniversityMunichGermany
  2. 2.Siemens AG CTMunichGermany
  3. 3.University of OxfordOxfordUK
  4. 4.Max-Planck Institut für InformatikSaarbrückenGermany
  5. 5.Digital Factory, Siemens PLM SoftwarePlanoUSA

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