Abstract
Recent knowledge graph embedding models have shown promising results on link prediction, by employing operations on quaternion space to capture correlations between entities. However, they only used three quaternion embeddings for rotation calculation that fails to capture the interaction between entities and relations. The single relation quaternion to rotate the head entity also makes the connection between the head and tail entities weak. To address the problem, this paper proposes a novel knowledge graph embedding model denoted as QuatPE, to utilize paired relations to simultaneously rotate the head and tail entities in a triple. We employ the adjustment vectors to adjust the position of the same entity in a quaternion space when facing different triples. With paired relations, QuatPE can strengthen the connection between the head and tail entities which enhances the representation capabilities. By using the adjustment vectors, QuatPE also helps to better handle complex relation patterns, such as 1-to-N, N-to-1, and N-to-N. Experimental results show that QuatPE can achieve significant performance on well-known datasets for link prediction. Researchers can reproduce our results by following the source codes at https://github.com/galaxysunwen/QuatPE-master.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, Z., Xu, Q., Yang, Z., Cao, X., Huang, Q.: Dual quaternion knowledge graph embeddings. Proceedings of the AAAI, vol. 35, no. 8, pp. 6894–6902 (2021)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Sun, Z., Deng, Z.-H., Nie, J.-Y., Tang, J.: RotatE: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of ICML 2016, pp. 2071–2080 (2016)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of AAAI, vol. 32, no. 1 (2018)
Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embeddings. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI (2015)
Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of NAACL-HLT (2018)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI, vol. 28, no. 1 (2014)
Zhang, Z., Cai, J., Zhang, Y., Wang, J.: Learning hierarchy-aware knowledge graph embeddings for link prediction. Proc. AAAI 34(03), 3065–3072 (2020)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL-IJCNLP, pp. 687–696 (2015)
Nguyen, D.Q., Vu, T., Nguyen, T.D., Phung, D.: QuatRE: relation-aware quaternions for knowledge graph embeddings. In: Proceedings of WWW (2022)
Gao, H., Yang, K., Yang, Y., Zakari, R.Y., Owusu, J.W., Qin, K.: QuatDE: dynamic quaternion embedding for knowledge graph completion. arXiv preprint arXiv:2105.09002 (2021)
Bastos, A., Singh, K., Nadgeri, A., Shekarpour, S., Mulang, I.O., Hoffart, J.: HopfE: knowledge graph representation learning using inverse hopf fibrations. In: Proceedings of CIKM, pp. 89–99 (2021)
Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., Ré, C.: Low-dimensional hyperbolic knowledge graph embeddings. In: Proceedings of ACL (2020)
Yang, B., Yih, W.-T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of ICLR (2015)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proceedings of ICML, pp. 809–816 (2011)
Balažević, I., Allen, C., Hospedales, T.: “Tucker”: tensor factorization for knowledge graph completion. In: Proceedings of EMNLP-IJCNLP, pp. 5185–5194 (2019)
Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., Talukdar, P.: InteractE: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI, vol. 34, no. 03, pp. 3009–3016 (2020)
Zhang, S., Sun, Z., Zhang, W.: Improve the translational distance models for knowledge graph embedding. J. Intell. Inf. Syst. 55(3), 445–467 (2020)
Gao, C., Sun, C., Shan, L., Lin, L., Wang, M.: Rotate3D: representing relations as rotations in three-dimensional space for knowledge graph embedding. In: Proceedings of CIKM, pp. 385–394 (2020)
Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.: Composition-based multi-relational graph convolutional networks. In: Proceedings of ICLR (2019)
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of CVSC, pp. 57–66 (2015)
Wang, K., Liu, Y., Xu, X., Lin, D.: Knowledge graph embedding with entity neighbors and deep memory network. arXiv preprint arXiv:1808.03752 (2018)
Sadeghi, A., Collarana, D., Graux, D., Lehmann, J.: Embedding knowledge graphs attentive to positional and centrality qualities. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12976, pp. 548–564. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86520-7_34
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation. In: Proceedings of WWW, pp. 2000–2010 (2019)
Cheng, H.-T., et al.: Wide & deep learning for recommender systems. In: Proceedings of DLRS, pp. 7–10 (2016)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 1–22 (2012)
Acknowledgements
This research was supported by the School of Film Xiamen University - Dongbo Future Artificial Intelligence Research Institute Co., Ltd. Joint Laboratory for created the Metaverse (School Agreement No. 20223160C0026), School of Film Xiamen University - Xiaozhi Deep Art Artificial Intelligence Research Institute Co., Ltd. Computational Art Joint Laboratory (School Agreement No. 20213160C0032), and School of Informatics Xiamen University - Xiamen Yinjiang Smart City Joint Research Center (School Agreement No. 20213160C0029). Xiaoli Wang was supported by the Natural Science Foundation of Fujian Province of China (No. 2021J01003).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, W., Wu, Q., Wang, X., Yao, J., Bao, Z. (2023). Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-46674-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46673-1
Online ISBN: 978-3-031-46674-8
eBook Packages: Computer ScienceComputer Science (R0)