[19] Wessel Radstok and Melisachew Wudage Chekol. Leveraging static models for link prediction in temporal knowledge graphs. 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 1034-1041, 2021. URL https://api_semanticscholar.org/CorpusID:235670110.
[20] Chengjin Xu, M. Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. Tero: A time-aware knowledge graph embedding via temporal rotation. In International Conference on Computational Linguistics, 2020. URL https://api-semanticscholar.org/CorpusID:222124934.
[21] 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, 2018.
[22] Chengjin Xu, M. Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. Temporal knowledge graph completion based on time series gaussian embedding. In International Workshop on the Semantic Web, 2019. URL https://api-semanticscholar.org/CorpusID:218900866.
[23] Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion. ArXiv, abs/2011.03984, 2020. URL https://api-semanticscholar.org/CorpusID:226262324.
[24] Rakshit S. Trivedi, Hanjun Dai, Yichen Wang, and Le Song. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In International Conference on Machine Learning, 2017. URL https://api-semanticscholar.org/CorpusID:8040343.
[25] Jiapeng Wu, Mengyao Cao, Jackie Chi Kit Cheung, and William L. Hamilton. Temp: Temporal message passing for temporal knowledge graph completion. In Conference on Empirical Methods in Natural Language Processing, 2020. URL https://api_semanticscholar.org/CorpusID:222177069.
[26] Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Wang Haotian, and Yang Jinrui. Rtfe: A recursive temporal fact embedding framework for temporal knowledge graph completion. In North American Chapter of the Association for Computational Linguistics, 2020. URL https://api-semanticscholar.org/CorpusID:222067007.
[27] Siyuan Liao, Shangsong Liang, Zaiqiao Meng, and Qiang Zhang. Learning dynamic embeddings for temporal knowledge graphs. Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021. URL https://api_semanticscholar.org/CorpusID:232126110.
[28] Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. In Conference on Empirical Methods in Natural Language Processing, 2019. URL https://api_semanticscholar.org/CorpusID:222205878.
[29] Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuzhuo 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.
[30] Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In Conference on Empirical Methods in Natural Language Processing, 2021. URL https://api_semanticscholar.org/CorpusID: 237304520.
[31] Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In International Conference on Learning Representations, 2021. URL https://api-semanticscholar.org/CorpusID:235614395.
[32] Jaehun Jung, Jinhong Jung, and U Kang. Learning to walk across time for interpretable temporal knowledge graph completion. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021. URL https://api_semanticscholar.org/CorpusID:236979995.
[33] Ruijie Wang, Zheng Li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, and Tarek Abdelzaher. Learning to sample and aggregate: Few-shot reasoning over temporal knowledge graphs. Advances in Neural Information Processing Systems, 35:16863-16876, 2022.
[34] Nilesh Dalvi and Dan Suciu. Efficient query evaluation on probabilistic databases. The VLDB Journal, 16: 523-544, 2007.