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Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Introducing acled: An armed conflict location and event dataset
Clionadh Raleigh, rew Linke, Håvard Hegre, and Joakim Karlsen. 2010 · 2010
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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ICEWS Coded Event Data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol. 2018 · 2018
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Tucker: Tensor factorization for knowledge graph completion
Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. 2019 · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019 · 2019
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Quaternion knowledge graph embeddings
Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs
Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Explainable subgraph reasoning for forecasting on temporal knowledge graphs
Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2021a · 2021
Cited alongside, same era.
Learning neural ordinary equations for forecasting future links on temporal knowledge graphs
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021b · 2021
Cited alongside, same era.
Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs
Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, and Xueqi Cheng. 2021a · 2021
Cited alongside, same era.
Temporal knowledge graph reasoning based on evolutional representation learning
Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. 2021b · 2021
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
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Incorporating structured sentences with time-enhanced BERT for fully-inductive temporal relation prediction
Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, and Yong Dou. 2023a · 2023
Closest in time.
Meta-learning based knowledge extrapolation for temporal knowledge graph
Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, and Yong Dou. 2023b · 2023
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Learning meta-representations of one-shot relations for temporal knowledge graph link prediction
Zifeng Ding, Bailan He, Jingpei Wu, Yunpu Ma, Zhen Han, and Volker Tresp. 2023a · 2023
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Improving few-shot inductive learning on temporal knowledge graphs using confidence-augmented reinforcement learning
Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, and Volker Tresp. 2023c · 2023
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One-shot learning for temporal knowledge graphs
Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren, Fred Morstatter, and Aram Galstyan. 2021 · 2021
Cited alongside, same era.
Question answering over temporal knowledge graphs
Apoorv Saxena, Soumen Chakrabarti, and Partha P. Talukdar. 2021 · 2021
Cited alongside, same era.
TimeTraveler: Reinforcement learning for temporal knowledge graph forecasting
Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, and Kun He. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
Cited alongside, same era.
Few-shot inductive learning on temporal knowledge graphs using concept-aware information
Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, and Volker Tresp. 2022 · 2022
Cited alongside, same era.
Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning
Yujia Li, Shiliang Sun, and Jing Zhao. 2022 · 2022
Cited alongside, same era.
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Comparing apples and oranges? on the evaluation of methods for temporal knowledge graph forecasting
Julia Gastinger, Timo Sztyler, Lokesh Sharma, Anett Schuelke, and Heiner Stuckenschmidt. 2023 · 2023
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ECOLA: Enhancing temporal knowledge embeddings with contextualized language representations
Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Koeppl, Hinrich Schütze, and Volker Tresp. 2023 · 2023
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Temporal knowledge graph forecasting without knowledge using in-context learning
Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, and Jay Pujara. 2023 · 2023
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Gentkg: Generative forecasting on temporal knowledge graph
Ruotong Liao, Xu Jia, Yunpu Ma, and Volker Tresp. 2023 · 2023
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RETIA: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation
Kangzheng Liu, Feng Zhao, Guandong Xu, Xianzhi Wang, and Hai Jin. 2023 · 2023
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One-shot relational learning for extrapolation reasoning on temporal knowledge graphs
Ruixin Ma, Biao Mei, Yunlong Ma, Hongyan Zhang, Meihong Liu, and Liang Zhao. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Pre-trained language model with prompts for temporal knowledge graph completion
Wenjie Xu, Ben Liu, Miao Peng, Xu Jia, and Min Peng. 2023a · 2023
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Temporal knowledge graph reasoning with historical contrastive learning
Yi Xu, Junjie Ou, Hui Xu, and Luoyi Fu. 2023b · 2023
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