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A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\emph{subject}, \emph{relation}, \emph{object}, \emph{timestamp}) to describe dynamic facts.
Recurrent event network: Autoregressive structure inference over temporal knowledge graphs
Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2019 · 1904
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Graph hawkes neural network for forecasting on temporal knowledge graphs
Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, and Volker Tresp. 2020c · 2003
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Tensor decompositions for temporal knowledge base completion
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Hyte: Hyperplane-based temporally aware knowledge graph embedding
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Adam: A method for stochastic optimization
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Convolutional 2d knowledge graph embeddings
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Learning sequence encoders for temporal knowledge graph completion
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Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2018 · 2018
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End-to-end structure-aware convolutional networks for knowledge base completion
Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs
Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020 · 2020
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Query graph generation for answering multi-hop complex questions from knowledge bases
Yunshi Lan and Jing Jiang. 2020 · 2020
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Temp: Temporal message passing for temporal knowledge graph completion
Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L Hamilton. 2020 · 2020
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Learning neural ordinary equations for forecasting future links on temporal knowledge graphs
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021a · 2021
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Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021b · 2021
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Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019 · 2019
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Dynamic knowledge graph based multi-event forecasting
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Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion
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Explainable subgraph reasoning for forecasting on temporal knowledge graphs
Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2020b
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Learning dynamic embeddings for temporal knowledge graphs
Siyuan Liao, Shangsong Liang, Zaiqiao Meng, and Qiang Zhang. 2021 · 2021
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Timetraveler: Reinforcement learning for temporal knowledge graph forecasting
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Time-aware graph embedding: A temporal smoothness and task-oriented approach
Yonghui Xu, Shengjie Sun, Huiguo Zhang, Chang’an Yi, Yuan Miao, Dong Yang, Xiaonan Meng, Yi Hu, Ke Wang, Huaqing Min, et al. 2021 · 2021
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Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks
Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhang. 2021 · 2021
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Complex evolutional pattern learning for temporal knowledge graph reasoning
Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, and Xueqi Cheng. 2022 · 2022
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Complex embeddings for simple link prediction
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