Abstract
In the cognitive intelligence of machines, more and more researchers use knowledge graphs that represent the structure of entities. The basic idea of knowledge representation learning is to project knowledge (entities and relations) into a vector space. The entities and relationships embedded in the 2-D space use convolutional neural networks to obtain the mutual information between them. However, the feature extraction capabilities of neural networks are limited by the input feature information. Therefore, our model named knowledge graph embedding based on quaternion transformation and convolutional neural network (QCKGE), aims to solve the limitation of input features by riching entities and relations mutual features. Firstly, the entity and relation are expressed as a two-dimensional real number space. Then, these features are mapped to the quaternion space to increase the ability to express entities and relations. Finally, the convolutional neural networks to mine richer interactive information in entities and relationships for link prediction. Besides, this paper also uses multi-layer convolutional neural network to mine deeper information hidden in the features. On the datasets of WN18, FB15k, WN18RR, and FB15k-237, compared with the existing model, our design can show a better result.
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
Newell, A., Shaw, J.C., Simon, H.A.: Report on a general problem solving program. In: Proceedings of the 1st International Conference on Information Processing, pp. 256–264 (1959)
Ji, S., Pan, S., Cambria, E.: A survey on knowledge graphs: representation, acquisition and applications. IEEE Trans. Neural Netw. Learn. Syst., 1–21 (2021)
Antoine, B., Nicolas, U., Alberto, G.D.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Wang, Z., Zhang, J.W., Feng J.L.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Lin, Y.K., Liu Z.Y., Sun, M.S.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence Learning, pp. 2181–2187 (2015)
Ji, G.L., He, S.Z., Xu, L.H.: 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)
Nguyen, D.Q., Sirts, K., Qu, L.Z.: STransE: a novel embedding model of entities and relationships in knowledge bases. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 460–466 (2016)
Yang, B., Yih, W.T., He, X.D., Gao, J.F.: Embedding entities and relations for learning and inference in knowledge bases. ArXiv preprint arXiv: 1412.6575 (2015)
Dettmers, T., Minervini, P., Stenetorp, P.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 1811–1818 (2018)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Nguyen, D.Q., Vu, T., Nguyen, T.D.: A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2180–2189 (2019)
Nguyen, D.Q., Nguyen, D.Q., Nguyen, T.D.: Convolutional neural network-based model for knowledge base completion and its application to search personalization. Semantic Web 10(5), 947–960 (2019)
Vashishth, S., Sanyal, S., Nitin, V.: InteractE: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: International Conference on Learning Representations, vol. 34, no. 03, pp. 3009–3016 (2020)
Trouillon, T., Welbl, J., Riedel, S.: Complex embeddings for simple link prediction. In: Proceedings of the 33nd International Conference on Machine Learning, pp. 2071–2080 (2016)
Sun, Z.Q., Deng, Z.H., Nie, J.Y.: Rotate: knowledge graph embedding by relational rotation in complex space. In: International Conference on Learning Representations, pp. 1–18 (2019)
Zhu, X., Xu, Y., Xu, H., Chen, C.: Quaternion convolutional neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 645–661. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_39
Hamilton, W.R.: Elements of Quaternions. Longmans Green, London (1866)
Parcollet, T., Ravanelli, M., Morchid, M.: Quaternion recurrent neural networks. In: International Conference on Learning Representations, pp. 1–19 (2019)
He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, pp. 1–15 (2015)
Toutanova, K., Chen, D.Q.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 57–66 (2015)
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 1–7 (2016)
Ebisu, T., Ichise, R.: TorusE: knowledge graph embedding on a lie group. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 1–8 (2018)
Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems, pp. 4289–4300 (2018)
Wang, K., Liu, Y., Xu, X.J.: Knowledge graph embedding with entity neighbors and deep memory network. ArXiv preprint arXiv:1808.03752 (2018)
Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q.: A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 327–333 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, Y., Tian, X., Zhou, J., Zheng, B., Li, H., Zhu, Z. (2022). Knowledge Graph Embedding Based on Quaternion Transformation and Convolutional Neural Network. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-95408-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95407-9
Online ISBN: 978-3-030-95408-6
eBook Packages: Computer ScienceComputer Science (R0)