Advertisement

Cross-Projection for Embedding Translation in Knowledge Graph Completion

  • Xiangnan Ma
  • Wenting Yu
  • Lin Zhu
  • Luyi BaiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Knowledge graphs have been applied in many applications. However, they are incomplete. Previous completion works such as TransE, TransH and TransR/CTransR regard relation as translation between head entity and tail entity. They think that only entities are affected by relations, and change entities embeddings according to the corresponding relation. However, they neglect the influence between two entities. In this paper, we propose a new model named Cross-projected Translation Embedding model (CpTE) based on the translation theory. In CpTE, we assume that relation is affected by related entities and transmits effect between end-to-end entities. Head entity and tail entity are influenced by each other rather than their relation. Given a triple, we project each entity into another entity space and map relation into a union space constructed by entities. In Experiments, we evaluate our model on two typical tasks including link prediction and triple classification. Experiments results show that our approach outperforms than TransE, TransH and TransR/CTransR in link prediction task on evaluation sets WN18 and FB15K, and gets an improvement in triple classification on specific evaluation set FB15K.

Keywords

Knowledge graph Representation learning Translation embedding 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2019501030), the Natural Science Foundation of Liaoning Province (2019-MS-130), and the Fundamental Research Funds for the Central Universities (N172304026). The data sets and basic codes used in our model are from OpenKE [16] project. We thank all teamers of OpenKE project for their work.

References

  1. 1.
    Miller, G.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  2. 2.
    Bollacker, K., Evans, C., Paritosh, P., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)Google Scholar
  3. 3.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge unifying wordnet and wikipedia. In: 16th International World Wide Web Conference, pp. 697–706 (2007)Google Scholar
  4. 4.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, pp. 809–816 (2011)Google Scholar
  5. 5.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008)Google Scholar
  6. 6.
    Bordes, A., Glorot, X., Weston, J., et al.: Joint learning of words and meaning representations for open-text semantic parsing. In: 15th International Conference on Artificial Intelligence and Statistics, pp. 127–135 (2012)Google Scholar
  7. 7.
    Bordes, A., Glorot, X., Weston, J., et al.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bordes, A., Weston, J., Collobert, R., et al.: Learning structured embeddings of knowledge bases. In: Advances in Neural Information Processing Systems, pp. 301–306 (2011)Google Scholar
  9. 9.
    Socher, R., Chen, D., Manning, C.D., et al.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)Google Scholar
  10. 10.
    Jenatton, R., Roux, N.L., Bordes, A., et al.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems, pp 3167–3175 (2012)Google Scholar
  11. 11.
    Bordes, A., Usunier, N., Garcia-Duran, A., et al.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)Google Scholar
  12. 12.
    Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. In: 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)Google Scholar
  13. 13.
    Lin, Y., Zhang, J., Liu, Z., et al.: Learning entity and relation embeddings for knowledge graph completion. In: 29th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)Google Scholar
  14. 14.
    Ji, G., He, S., Xu, L., Liu, K., et al.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 687–696 (2015)Google Scholar
  15. 15.
    Ji, G., Liu, K., He, S., et al.: Knowledge graph completion with adaptive sparse transfer matrix. In: 30th AAAI Conference on Artificial Intelligence. pp. 985–991. (2016)Google Scholar
  16. 16.
    OpenKE Homepage. http://openke.thunlp.org. Accessed 16 Mar 2019

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer and Communication EngineeringNortheastern UniversityQinhuangdaoChina

Personalised recommendations