Geometric Connectionist Machines for Triple Classification

  • Tiansi DongEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 910)


This chapter is the continued discussion on the last experiment in Chap.  6—Under what condition, can the precision for the Task of Membership-Validation reach 100%? We will create a new type of Geometric Connectionist Machines for Triple Classification task in Knowledge Graph reasoning. Our key question is: How shall we spatialize labeled tree structures onto vector embeddings?


  1. Auer, S., Bizer, C., Lehmann, J., Kobilarov, G., Cyganiak, R., & Ives, Z. (2007). DBpedia: A nucleus for a web of open data. In K. Aberer, K.-S. Choi, N. Noy, D. Allemang, K.-I. Lee, L. Nixon, J. Golbeck, P. Mika, D. Maynard, R. Mizoguchi, G. Schreiber & P. Cudré-Mauroux (Eds.) ISWC07. Springer.Google Scholar
  2. Bishop, C. M. (2006). Pattern recognition and machine learning. Secaucus, NJ, USA: Springer.zbMATHGoogle Scholar
  3. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008). Freebase: A collaboratively created graph database for structuring human knowledge. In SIGMOD ’08 (pp. 1247–1250) New York, NY, USA: ACM.Google Scholar
  4. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modelling multi-relational data. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.) Advances in Neural Information Processing Systems, 26, 2787–2795. Curran Associates, Inc.Google Scholar
  5. Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E., & Smith, N. A. (2015). Retrofitting word vectors to semantic lexicons. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1606–1615). ACL.Google Scholar
  6. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.Google Scholar
  7. Han, X., Liu, Z., & Sun, M. (2016). Joint representation learning of text and knowledge for knowledge graph completion. CoRR. abs/1611.04125.Google Scholar
  8. He, S., Liu, K., Ji, G., & Zhao, J. (2015). Learning to represent knowledge graphs with gaussian embedding. In CIKM’15 (pp. 623–632). New York, USA: ACM.Google Scholar
  9. Ji, G., He, S., Xu, L., Liu, K., & Zhao, J. (2015). Knowledge graph embedding via dynamic mapping matrix. In ACL’2015 (pp. 687–696). Beijing: ACL.Google Scholar
  10. Manning, C. D., Raghavan, P., & Schütze, H. (2008b). Introduction to information retrieval. New York, NY, USA: Cambridge University Press.CrossRefGoogle Scholar
  11. Miller, G. A. (1995). Wordnet: A lexical database for english. Communications of the ACM, 38(11), 39–41.CrossRefGoogle Scholar
  12. Nickel, M., & Kiela, D. (2017). Poincaré embeddings for learning hierarchical representations. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan & R. Garnett (Eds.). Advances in Neural Information Processing Systems (Vol. 30, pp. 6338–6347). Curran Associates, Inc.Google Scholar
  13. Socher, R., Chen, D., Manning, C. D., & Ng, A. (2013). Reasoning with neural tensor networks for knowledge base completion. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.) Advances in Neural Information Processing Systems, 26, 926–934. Curran Associates, Inc.Google Scholar
  14. Speer, R., Chin, J., & Havasi, C. (2017). Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4–9, 2017, San Francisco, CA, USA (pp. 4444–4451).Google Scholar
  15. Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). Yago: A core of semantic knowledge. In WWW ’07 (pp 697–706). New York, NY, USA: ACM.Google Scholar
  16. Wang, Z., & Li, J. (2016). Text-enhanced representation learning for knowledge graph. IJCAI, 1293–1299.Google Scholar
  17. Wang, Z., Zhang, Feng, J., & Chen, Z. (2014a). Knowledge graph and text jointly embedding. In EMNLP, 1591–1601.Google Scholar
  18. Xiao, H., Huang, M., & Zhu, X. (2016). From one point to a manifold: Knowledge graph embedding for precise link prediction. IJCAI, 1315–1321.Google Scholar
  19. Xie, R., Liu, Z., Jia, J., Luan, H., & Sun, M. (2016). Representation learning of knowledge graphs with entity descriptions. AAAI, 2659–2665.Google Scholar
  20. Zhang, D., Yuan, B., Wang, D., & Liu, R. (2015). Joint semantic relevance learning with text data and graph knowledge. ACL-IJCNLP, 32–40.Google Scholar
  21. Zhong, H., Zhang, J., Wang, Z., Wan, H., & Chen, Z. (2015). Aligning knowledge and text embeddings by entity descriptions. EMNLP, 267–272.Google Scholar

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© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.ML2R Competence Center for Machine Learning Rhine-Ruhr, MLAI Lab, AI Foundations Group, Bonn-Aachen International Center for Information Technology (b-it)University of BonnBonnGermany

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