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Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications.
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P. Ristoski, G. K. D. d. Vries, and H. Paulheim, “A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web,” in International semantic web conference . Springer, 2016, pp. 186–194
2016
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2017
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2017
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2018
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2018
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2018
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2018
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2018
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T. Ebisu and R. Ichise, “Generalized translation-based embedding of knowledge graph,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 5, pp. 941–951, 2019
2019
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Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “Rotate: Knowledge graph embedding by relational rotation in complex space,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=HkgEQnRqYQ
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2019
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2019
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2019
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2019
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2019
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2020
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2020
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2020
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2021
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Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in Proc. of WWW , 2021
2021
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2021
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Y. Shen, N. Ding, H.-T. Zheng, Y. Li, and M. Yang, “Modeling relation paths for knowledge graph completion,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 11, pp. 3607–3617, 2020
2020
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Z. Zhang, F. Zhuang, H. Zhu, Z. Shi, H. Xiong, and Q. He, “Relational graph neural network with hierarchical attention for knowledge graph completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 9612–9619
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2022
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