[35] Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In NIPS 2013., 2013.
[36] Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui. Towards time-aware knowledge graph completion. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1715-1724, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee. URL https://aclanthology.org/C16-1161.
[37] Elizabeth Boschee, Jennifer Lautenschlager, Sean O'Brien, Steve Shellman, James Starz, and Michael Ward. ICEWS Coded Event Data, 2015. URL https://doi.org/10.7910/DVN/28075.
[38] Kaleb Leetaru and Philip A. Schrodt. Gdelt: Global data on events, location, and tone. ISA Annual Convention, 2013.
[39] Pengpeng Shao, Guohua Yang, Dawei Zhang, Jianhua Tao, Feihu Che, and Tong Liu. Tucker decomposition-based temporal knowledge graph completion. Knowl. Based Syst., 238:107841, 2020.
[40] Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yanzhuo Wang, and Xueqi Cheng. Temporal knowledge graph reasoning based on evolutionary representation learning. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. URL https://api_semanticscholar.org/CorpusID:233324265.
[41] Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the International Conference on Learning Representations (ICLR) 2015, May 2015.
[42] Seyed Mehran Kazemi and David L. Poole. Simple embedding for link prediction in knowledge graphs. In Neural Information Processing Systems, 2018. URL https://api_semanticscholar.org/CorpusID:3674966.
[43] Yunpu Ma, Volker Tresp, and Erik A. Daxberger. Embedding models for episodic knowledge graphs. J. Web Semant., 59, 2018. URL https://api-semanticscholar.org/CorpusID:54444869.
[44] Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. HyTE: Hyperplane-based temporally aware knowledge graph embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2001–2011, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1225. URL https://aclanthology.org/D18-1225.
[45] Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. Learning sequence encoders for temporal knowledge graph completion. In Conference on Empirical Methods in Natural Language Processing, 2018. URL https://api_semanticscholar.org/CorpusID:52183483.
[46] Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. Diachronic embedding for temporal knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3988-3995, 2020.
[47] Youngjoo Seo, Michael Defferrard, Pierre Vandergheynst, and Xavier Bresson. Structured sequence modeling with graph convolutional recurrent networks. In International Conference on Neural Information Processing, 2016. URL https://api_semanticscholar.org/CorpusID:2687749.
[48] Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhan. Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. In AAAI Conference on Artificial Intelligence, 2020. URL https://api_semanticscholar.org/CorpusID:229180723.
[49] James F. Allen. Maintaining knowledge about temporal intervals. Commun. ACM, 26(11):832-843, nov 1983. ISSN 0001-0782. doi: 10.1145/182.358434. URL https://doi.org/10.1145/182.358434.
[50] Diederick P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR), 2015.