Abstract
Link prediction in Knowledge Graph, also called knowledge completion, is a significant problem in graph mining and has many applications for large companies. The more accurate the link prediction results will bring satisfaction, reduce and avoid risks, and commercial benefits. Almost all state-of-the-art models focus on the deep learning approach, especially using convolutional neural networks (CNN). By analysing the strengths and weaknesses of the CNN based models, we proposed a better model to improve the performance of the link prediction task. Specifically, we apply a CNN with specific filters generated through the Hypernetwork architecture. Moreover, we increase the depth of the model more than baseline models to help learn more helpful information. Experimental results show that the proposed model gets better results when compared to CNN-base models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Balažević, I., Allen, C., Hospedales, T.M.: Hypernetwork knowledge graph embeddings. In: Tetko, I., Kurková, V., Karpov, P., Theis, F. (eds.) International Conference on Artificial Neural Networks. LNCS, vol. 11731, pp. 553–565, Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30493-5_52
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS), pp. 1–9 (2013)
Nguyen, D.Q., Vu, T., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A capsule network based embedding model for knowledge graph completion and search personalization. In: NAACL-HLT, pp. 2180–2189 (2019b)
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7) (2011)
Ha, D., Dai, A., Le, Q.V.: Hypernetworks. In: International Conference on Learning Representations (2016)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1 (2015)
Nathani, D., Chauhan, J., Sharma, C., Kaul, M.: Learning attention based embeddings for relation prediction in knowledge graphs. In: ACL, pp. 4710–4723 (2019)
Nguyen, D.Q., Sirts, K., Qu, L., Johnson, M.: STransE: a novel embedding model of entities and relationships in knowledge bases. In: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 327–333 (2016)
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 the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 327–333 (2017)
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Discov. Data (TKDD) 15(2), 1–49 (2021)
Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embeddings. Adv. Neural Inf. Process. Syst. 32, 2735–2745 (2019)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)
Sun, Z., Deng, Z.H., Nie, J.Y. and Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: Seventh International Conference on Learning Representations, pp. 1–18 (2019)
Toutanova, K., Chen, D.: 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)
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR (2016)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1 (2014)
Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)
Zhang, Z., Cai, J., Wang, J.: Duality-induced regularizer for tensor factorization based knowledge graph completion. In: Advances in Neural Information Processing Systems, vol. 33 (2020)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Han, X., et al.: OpenKE: an open toolkit for knowledge embedding. In: Proceedings of EMNLP (2018)
Rossi, A., Firmani, D., Matinata, A., Merialdo, P., Barbosa, D.: Knowledge graph embedding for link prediction: a comparative analysis. 1(1), 43, Article no. 1 (2016)
Acknowledgements
This research is funded by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam, Grant number CNTT 2021-03 and Advanced Program in Computer Science.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Le, T., Nguyen, D., Le, B. (2021). Learning Embedding for Knowledge Graph Completion with Hypernetwork. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-88081-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88080-4
Online ISBN: 978-3-030-88081-1
eBook Packages: Computer ScienceComputer Science (R0)