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The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods dominate.
Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion
Zhen Han, Yunpu Ma, Peng Chen, and Volker Tresp. 2020b · 2011
Earlier work this paper cites.
Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion
Zhen Han, Yunpu Ma, Peng Chen, and Volker Tresp. 2020b · 2011
Earlier work this paper cites.
Gdelt: Global data on events, location, and tone, 1979–2012
Kalev Leetaru and Philip A Schrodt. 2013 · 2012
Earlier work this paper cites.
Gdelt: Global data on events, location, and tone, 1979–2012
Kalev Leetaru and Philip A Schrodt. 2013 · 2012
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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek. 2013 · 2013
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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 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
Earlier work this paper cites.
A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2015 · 2015
Earlier work this paper cites.
Learning with memory embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan Baier, and Denis Krompaß. 2015 · 2015
Earlier work this paper cites.
Icews coded event data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
Earlier work this paper cites.
A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2015 · 2015
Earlier work this paper cites.
Learning with memory embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan Baier, and Denis Krompaß. 2015 · 2015
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Embedding models for episodic knowledge graphs
Yunpu Ma, Volker Tresp, and Erik A Daxberger. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Embedding models for episodic knowledge graphs
Yunpu Ma, Volker Tresp, and Erik A Daxberger. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 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
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Re-gcn: relation enhanced graph convolutional network for entity alignment in heterogeneous knowledge graphs
Jinzhu Yang, Wei Zhou, Lingwei Wei, Junyu Lin, Jizhong Han, and Songlin Hu. 2020 · 2020
Earlier work this paper cites.
Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Re-gcn: relation enhanced graph convolutional network for entity alignment in heterogeneous knowledge graphs
Jinzhu Yang, Wei Zhou, Lingwei Wei, Junyu Lin, Jizhong Han, and Songlin Hu. 2020 · 2020
Earlier work this paper cites.
Learning neural ordinary equations for forecasting future links on temporal knowledge graphs
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
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. 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 neural ordinary equations for forecasting future links on temporal knowledge graphs
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 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. 2021 · 2021
Deep learning, reinforcement learning, and world models
Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, and Jun Morimoto. 2022 · 2022
Later among the works it cites.
Dialokg: Knowledge-structure aware task-oriented dialogue generation
Md Rashad Al Hasan Rony, Ricardo Usbeck, and Jens Lehmann. 2022 · 2022
Later among the works it cites.
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, et al. 2022 · 2022
Later among the works it cites.
Drlk: dynamic hierarchical reasoning with language model and knowledge graph for question answering
Miao Zhang, Rufeng Dai, Ming Dong, and Tingting He. 2022 · 2022
Later among the works it cites.
Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
Jinheon Baek, Alham Fikri Aji, and Amir Saffari. 2023 · 2023
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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.
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.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
Cited alongside, same era.
Enhanced temporal knowledge embeddings with contextualized language representations
Zhen Han, Ruotong Liao, Beiyan Liu, Yao Zhang, Zifeng Ding, Jindong Gu, Heinz Koeppl, Hinrich Schuetze, and Volker Tresp. 2022 · 2022
Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Closest in time.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Closest in time.
Zero-shot relational learning on temporal knowledge graphs with large language models
Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao, Bo Xiong, and Volker Tresp. 2023 · 2023
Closest in time.
Towards foundation models for knowledge graph reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, and Zhaocheng Zhu. 2023 · 2023
Closest in time.
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
Closest in time.
Temporal knowledge graph forecasting without knowledge using in-context learning
Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, and Jay Pujara. 2023 · 2023
Closest in time.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023 · 2023
Closest in time.
Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023 · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Closest in time.
Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2023 · 2023
Closest in time.
Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
Jinheon Baek, Alham Fikri Aji, and Amir Saffari. 2023 · 2023
Closest in time.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Closest in time.
Zero-shot relational learning on temporal knowledge graphs with large language models
Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao, Bo Xiong, and Volker Tresp. 2023 · 2023
Closest in time.
Towards foundation models for knowledge graph reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, and Zhaocheng Zhu. 2023 · 2023
Closest in time.
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
Closest in time.
Temporal knowledge graph forecasting without knowledge using in-context learning
Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, and Jay Pujara. 2023 · 2023
Closest in time.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023 · 2023
Closest in time.
Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023 · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Closest in time.
Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2023 · 2023
Closest in time.
A condensed transition graph framework for zero-shot link prediction with large language models
Mingchen Li, Chen Ling, Rui Zhang, and Liang Zhao. 2024 · 2024
Closest in time.
A condensed transition graph framework for zero-shot link prediction with large language models
Mingchen Li, Chen Ling, Rui Zhang, and Liang Zhao. 2024 · 2024
Closest in time.