├── .github └── ISSUE_TEMPLATE │ ├── bug_report.md │ └── feature_request.md ├── .gitignore ├── CITATION.cff ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── _config.yml ├── backup ├── Conference.md ├── header.md ├── papers.csv ├── rl.md ├── script.py └── years.md ├── conferences ├── 2014.md ├── 2016.md ├── 2017.md ├── 2018.md ├── 2019.md ├── 2020.md ├── 2021.md ├── 2022.md ├── 2023.md └── 2024.md └── journal ├── 2019.md ├── 2020.md ├── 2021.md ├── 2022.md ├── 2023.md ├── 2024.md └── 2025.md /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.ipynb 2 | __pycache__ -------------------------------------------------------------------------------- /CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 0.0.1 2 | message: "If you use this software, please cite it as below." 3 | authors: 4 | - family-names: "Le" 5 | given-names: "Nam" 6 | orcid: "https://orcid.org/0000-0002-2273-5089" 7 | title: "Recently working on Temporal Knowledge Graph Embedding and Reasoning" 8 | url: "https://github.com/stmrdus/tkger" -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, gender identity and expression, level of experience, 9 | education, socio-economic status, nationality, personal appearance, race, 10 | religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at {{ email }}. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 72 | 73 | [homepage]: https://www.contributor-covenant.org 74 | 75 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to TKGER 2 | 3 | Hi there! We're thrilled that you'd like to contribute to this project. We want to make contributing to this project as easy and transparent as 4 | possible. Your help is essential for keeping it great. 5 | 6 | Please note that this project is released with a [Contributor Code of Conduct](./CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms. 7 | 8 | ## Issues and PRs 9 | 10 | If you have suggestions for how this project could be improved, or want to report a bug, open an issue! We'd love all and any contributions. If you have questions, too, we'd love to hear them. 11 | 12 | We'd also love PRs. If you're thinking of a large PR, we advise opening up an issue first to talk about it, though! Look at the links below if you're not sure how to open a PR. 13 | 14 | We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue. 15 | 16 | ## Submitting a pull request 17 | 18 | 1. [Fork][fork] and clone the repository. 19 | 1. Create a new branch: `git checkout -b my-branch-name`. 20 | 1. Make your change 21 | 1. Push to your fork and [submit a pull request][pr]. 22 | 1. Pat your self on the back and wait for your pull request to be reviewed and merged. 23 | 24 | ## License 25 | 26 | By contributing to kbc, you agree that your contributions will be licensed under the [LICENSE](./LICENSE) file in the root directory of this source tree. 27 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | ![Github repo stars](https://img.shields.io/github/stars/stmrdus/tkger) 3 | ![GitHub last commit](https://img.shields.io/github/last-commit/stmrdus/tkger) 4 | 5 | GitHub top languageGitHub issues 6 | GitHub repo size 7 | GitHub last commit 8 | GitHub forks 9 | GitHub stars 10 | GitHub 11 | 12 | # TKGER 13 | 14 | Some papers on Temporal Knowledge Graph Embedding and Reasoning 15 | 16 | ## Useful research resources 17 | 18 | 1. Graph-based Deep Learning Literature, [Github](https://github.com/naganandy/graph-based-deep-learning-literature) 19 | 20 | _links to conference publications in graph-based deep learning_ 21 | 22 | 2. Reinforcement learning on graphs: A survey, [Github](https://github.com/neunms/Reinforcement-learning-on-graphs-A-survey) 23 | 24 | _This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers._ 25 | 26 | 3. Awesome Machine Learning for Combinatorial Optimization Resources, [Github](https://github.com/Thinklab-SJTU/awesome-ml4co) 27 | 28 | _Awesome machine learning for combinatorial optimization papers._ 29 | 30 | 4. Awesome-TKGC, [Github](https://github.com/jiapuwang/Awesome-TKGC) 31 | 32 | _A collection of papers and resources about temporal knowledge graph completion (TKGC)._ 33 | 34 | 5. AKGR: Awesome Knowledge Graph Reasoning, [Github](https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning) 35 | 36 | _AKGR: Awesome Knowledge Graph Reasoning is a collection of knowledge graph reasoning works, including papers, codes and datasets._ 37 | 38 | 6. Awesome Knowledge Graph, [Github](https://github.com/totogo/awesome-knowledge-graph) 39 | 40 | _A curated list of Knowledge Graph related learning materials, databases, tools and other resources._ 41 | 42 | 7. Awesome-DynamicGraphLearning, [Github](https://github.com/SpaceLearner/Awesome-DynamicGraphLearning) 43 | 44 | _Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs)._ 45 | 46 | 8. KGE, [Github](https://github.com/Yueshengxia/KGE) 47 | 48 | _Some papers on Knowledge Graph Embedding(KGE)_ 49 | 50 | 9. KGLQ, [Github](https://github.com/xinguoxia/KGLQ) 51 | 52 | _Some papers about Logical Query on Knowledge Graphs (KGLQ)_ 53 | 54 | 10. ADGC: Awesome Deep Graph Clustering, [Github](https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering) 55 | 56 | _Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets)._ 57 | 58 | 11. Graph Adversarial Learning Literature, [Github](https://github.com/safe-graph/graph-adversarial-learning-literature) 59 | 60 | _A curated list of adversarial attacks and defenses papers on graph-structured data._ 61 | 62 | ## Tutorial 63 | 64 | ### 2024 65 | 66 | [1] New Frontiers of Knowledge Graph Reasoning: Recent Advances and Future Trends - WWW 2024, [Webpage](https://lihuiliullh.github.io/) 67 | 68 | ### 2023 69 | 70 | [1] Knowledge Graph Reasoning and Its Applications - KDD 2023, [Webpage](https://sites.google.com/view/kg-reasoning/home) 71 | 72 | ### 2022 73 | 74 | [1] Reasoning on Knowledge Graphs: Symbolic or Neural? - AAAI 2022, [Webpage](https://aaai2022kgreasoning.github.io/) 75 | 76 | ### 2021 77 | 78 | [1] All You Need to Know to Build a Product Knowledge Graph - KDD 2021 Tutorial, [Webpage](https://naixlee.github.io/Product_Knowledge_Graph_Tutorial_KDD2021/) 79 | 80 | ### 2018 81 | 82 | [1] Fact Checking: Theory and Practice - KDD 2018 Tutorial, [Webpage](https://shiralkarprashant.github.io/fact-checking-tutorial-KDD2018/) 83 | 84 | ## [Survey Papers](#content) 85 | 86 | ### 2024 87 | 88 | 1. **Knowledge Graph Embedding: An Overview**, APSIPA Transactions on Signal and Information Processing, 2024. [paper](https://www.nowpublishers.com/article/OpenAccessDownload/SIP-2023-0065) 89 | _Ge, X., Wang, Y. C., Wang, B., & Kuo, C. C. J_ 90 | 91 | 2. **Survey of Temporal Knowledge Graph Completion Methods**, Journal of Computer Engineering & Applications, 2024. [paper](https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=10028331&AN=176129207&h=DToC9Tr7AnmJWLoaA0YmQwvLuqZfB5Mob0vIC4exEuOy6nI%2FQwPZGxv9jhrYizxjbbBYIwGfa8ISO34gRohnxw%3D%3D&crl=c) 92 | _Lei, X. I. A. O., & Qi, L. I._ 93 | 94 | 3. **Overview of Knowledge Reasoning for Knowledge Graph**, Neurocomputing, 2024. [paper](https://www.sciencedirect.com/science/article/pii/S0925231224003424) 95 | _Liu, X., Mao, T., Shi, Y., & Ren, Y._ 96 | 97 | 4. **Knowledge graph embedding: A survey from the perspective of representation spaces**, ACM Computing Surveys, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3643806) 98 | _Cao, J., Fang, J., Meng, Z., & Liang, S._ 99 | 100 | 5. **A survey on graph representation learning methods**, ACM Transactions on Intelligent Systems and Technology, 2024. [paper](https://dl.acm.org/doi/abs/10.1145/3633518) 101 | _Khoshraftar, S., & An, A._ 102 | 103 | 6. **Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey**, ArXiv, 2024. [paper](https://arxiv.org/abs/2402.05391) 104 | _Chen, Z., Zhang, Y., Fang, Y., Geng, Y., Guo, L., Chen, X., ... & Chen, H._ 105 | 106 | 7. **A survey for managing temporal data in RDF**, Information Systems, 2024. [paper](https://www.sciencedirect.com/science/article/pii/S0306437924000267) 107 | _Wu, Di, Hsien-Tseng Wang, and Abdullah Uz Tansel_ 108 | 109 | 8. **A Survey on Temporal Knowledge Graph: Representation Learning and Applications**, ArXiv, 2024. [paper](https://arxiv.org/abs/2403.04782) 110 | _Cai, Li, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, and Man Lan._ 111 | 112 | 9. **A survey of inductive knowledge graph completion**. Neural Computing and Applications, 36(8), 3837-3858, 2024. [paper](https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s00521-023-09286-2&casa_token=0CojBb7x7e4AAAAA:D6ZmmIdunksdtDal1bnlv-kniB7uSh20ghORVye4p8XW55UnY-Su2KqkpopQEX6Upea4mIkJmd2Dyvgv) 113 | _Liang, X., Si, G., Li, J., Tian, P., An, Z., & Zhou, F._ 114 | 115 | 10. **Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications**. International Journal of Machine Learning and Cybernetics, 1-20., 2024. [paper](https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1007/s13042-024-02106-6&casa_token=aHFdBA3-Z4oAAAAA:SbDNlbpjx2MAJ6htmB1GDOTUYUOQuWNJ6x0Xy0vIfjstxtUNsVRLkRP5r9zvahzClb7nLTfP1q5Nt-Xp) 116 | _Chen, C., Zheng, F., Cui, J., Cao, Y., Liu, G., Wu, J., & Zhou, J._ 117 | 118 | 11. **A comprehensive survey on deep graph representation learning**. Neural Networks, 106207, 2024. [paper](https://www.sciencedirect.com/science/article/pii/S089360802400131X) 119 | _Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., ... & Zhang, M._ 120 | 121 | 12. **Knowledge Graph Embedding: An Overview**. APSIPA Transactions on Signal and Information Processing, 13(1), 2024. [paper](https://www.nowpublishers.com/article/OpenAccessDownload/SIP-2023-0065) 122 | _Ge, X., Wang, Y. C., Wang, B., & Kuo, C. C. J._ 123 | 124 | 13. **Temporal Knowledge Graph Question Answering: A Survey**. arXiv preprint arXiv:2406.14191. [paper]() 125 | _Su, M., Li, Z., Chen, Z., Bai, L., Jin, X., & Guo, J._ 126 | 127 | 14. **Temporal Knowledge Graph Reasoning: A Review**. ig Data and Social Computing. BDSC 2024. [paper](https://link.springer.com/chapter/10.1007/978-981-97-5803-6_26) 128 | _Yu, C., Luo, T., Wang, J., Cao, Z._ 129 | 130 | 15. **Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey**. ACM Transactions on Knowledge Discovery from Data. [paper](https://dl.acm.org/doi/abs/10.1145/3686806) 131 | _Cheng, K., Ahmed, N. K., Rossi, R. A., Willke, T., & Sun, Y. (2024)._ 132 | 133 | 16. **A survey on temporal knowledge graph embedding: Models and applications**. Knowledge-Based Systems (2024): 112454. [paper](https://www.sciencedirect.com/science/article/pii/S0950705124010888) 134 | _Zhang, Yuchao, Xiangjie Kong, Zhehui Shen, Jianxin Li, Qiuhua Yi, Guojiang Shen, and Bo Dong_ 135 | 136 | 137 | ### 2023 138 | 139 | 1. **A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects**, ArXiv, 2023. [paper](https://arxiv.org/abs/2308.02457) 140 | 141 | _Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, Wen Gao_ 142 | 143 | 2. **Knowledge Graphs: Opportunities and Challenges**, Artificial Intelligence Review, 2023, [paper](https://link.springer.com/article/10.1007/s10462-023-10465-9#citeas) 144 | 145 | _Ciyuan Peng, Feng Xia, Mehdi Naseriparsa & Francesco Osborne_ 146 | 147 | 3. **Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs**, ArXiv, 2023. [paper](https://arxiv.org/abs/2302.01859) 148 | 149 | _Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen_ 150 | 151 | 4. **A Comprehensive Survey on Automatic Knowledge Graph Construction**, ArXiv, 2023. [paper](https://arxiv.org/abs/2302.05019) 152 | 153 | _Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu_ 154 | 155 | ### 2022 156 | 157 | 1. **Temporal Knowledge Graph Completion: A Survey** ArXiv, 2022. [paper](https://arxiv.org/abs/2201.08236) 158 | 159 | _Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li._ 160 | 161 | > **Update**: Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li, **Temporal Knowledge Graph Completion: A Survey**, 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. Pages 6545-6553. [paper](https://doi.org/10.24963/ijcai.2023/734) 162 | 163 | 2. **Reasoning over different types of knowledge graphs: Static, temporal and multi-modal**, ArXiv, 2022. [paper](https://arxiv.org/pdf/2212.05767) 164 | 165 | 3. **A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks**, Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, [paper](https://link.springer.com/chapter/10.1007/978-3-031-20738-9_110) 166 | 167 | _Sulin Chen & Jingbin Wang_ 168 | 169 | 4. **Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs**. 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. [paper](https://arxiv.org/abs/2302.01859) 170 | 171 | _Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen_ 172 | 173 | ### 2021 174 | 175 | 1. **Survey on Temporal Knowledge Graph**, 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC). [paper](https://ieeexplore.ieee.org/abstract/document/9750459) 176 | 177 | _Chong Mo; Ye Wang; Yan Jia; Qing Liao_ 178 | 179 | ## Datasets 180 | 181 | | Name | #Entities | #Relations | #Timestamps | #Collections | Timestamp | Link download | 182 | | ---------- | --------- | ---------- | ----------- | ------------ | --------- | ------------------------------------------------------------------------------------------------------------------------------------------ | 183 | | ICEWS14 | 7128 | 230 | 365 | 90730 | point | [https://paperswithcode.com/sota/link-prediction-on-icews14-1](https://paperswithcode.com/sota/link-prediction-on-icews14-1) | 184 | | ICEWS05-15 | 10488 | 251 | 4017 | 479329 | point | [https://paperswithcode.com/sota/link-prediction-on-icews05-15-1](https://paperswithcode.com/sota/link-prediction-on-icews05-15-1) | 185 | | ICEWS18 | 23033 | 256 | 304 | 468558 | point | [https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html](https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html) | 186 | | GDELT | 500 | 20 | 366 | 3419607 | point | [https://www.gdeltproject.org/](https://www.gdeltproject.org/) | 187 | | YAGO15k | 15403 | 32 | 169 | 138048 | interval | [https://paperswithcode.com/sota/link-prediction-on-yago15k-1](https://paperswithcode.com/sota/link-prediction-on-yago15k-1) | 188 | | WIKIDATA | 11153 | 96 | 328 | 150079 | interval | [https://www.wikidata.org/wiki/Wikidata:Main_Page](https://www.wikidata.org/wiki/Wikidata:Main_Page) 189 | 190 | ## 2025 191 | 192 | **Neural Networks** 193 | 194 | [1] Bai, L., Han, S., & Zhu, L. (2025). [Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0893608024009109). Neural Networks, 183, 106981. 195 | 196 | [2] Chen, T., Yang, L., Wang, Z., & Long, J. (2025). A rule-and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs. Neural Networks, 107186. 197 | 198 | **Science China Information Sciences** 199 | 200 | [1] Cai, W., Li, M., Shi, X., Fan, Y., Zhu, Q., & Jin, H. (2025). RE-SEGNN: recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting. Science China Information Sciences, 68(2), 122104. 201 | 202 | **Expert Systems With Applications** 203 | 204 | [1] Ma, R., Wang, L., Wu, H., Gao, B., Wang, X., & Zhao, L. (2025). Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning. Expert Systems With Applications, 260, 125366. 205 | 206 | **Information Processing & Management** 207 | 208 | [1] Bai, L., Zhang, H., An, X., & Zhu, L. (2025). Few-shot multi-hop reasoning via reinforcement learning and path search strategy over temporal knowledge graphs. Information Processing & Management, 62(3), 104001. 209 | 210 | [2] Li, Q., & Wu, G. (2025). Explainable reasoning over temporal knowledge graphs by pre-trained language model. Information Processing & Management, 62(1), 103903. 211 | 212 | [3] Xu, W., Liu, B., Peng, M., Jiang, Z., Jia, X., Liu, K., ... & Peng, M. (2025). Historical facts learning from Long-Short Terms with Language Model for Temporal Knowledge Graph Reasoning. Information Processing & Management, 62(3), 104047. 213 | 214 | **Pattern Recognition** 215 | 216 | [1] Zhang, J., Hui, B., Zhu, X., Tian, L., & Hua, F. (2025). Temporal knowledge graph reasoning based on discriminative neighboring semantic learning. Pattern Recognition, 111392. 217 | 218 | **IEEE Transactions on Neural Networks and Learning Systems** 219 | 220 | [1] Qian, Y., Wang, X., Sun, F., & Pan, L. (2025). Compressing Transfer: Mutual Learning-Empowered Knowledge Distillation for Temporal Knowledge Graph Reasoning. IEEE Transactions on Neural Networks and Learning Systems. 221 | 222 | **Information Fusion** 223 | 224 | [1] Yang, R., Zhu, J., Man, J., Liu, H., Fang, L., & Zhou, Y. (2025). GS-KGC: A generative subgraph-based framework for knowledge graph completion with large language models. Information Fusion, 117, 102868. 225 | 226 | **Knowledge-Based Systems** 227 | 228 | [1] Zhu, J., Hu, J., Bai, D., Fu, Y., Zhou, J., & Chen, D. (2025). Multi-dimension rotations based on quaternion system for modeling various patterns in temporal knowledge graphs. Knowledge-Based Systems, 113114. 229 | 230 | **PeerJ Computer Science** 231 | 232 | [1] Liu, W., Hasikin, K., Khairuddin, A. S. M., Liu, M., & Zhao, X. (2025). A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning. PeerJ Computer Science, 11, e2595. 233 | 234 | **Data & Knowledge Engineering** 235 | 236 | [1] Zhu, L., Duan, X., & Bai, L. (2025). SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph. Data & Knowledge Engineering, 155, 102372. 237 | 238 | **Symmetry** 239 | 240 | [1] Chen, Y., Li, X., Liu, Y., & Hu, T. (2025). Integrating Transformer Architecture and Householder Transformations for Enhanced Temporal Knowledge Graph Embedding in DuaTHP. Symmetry, 17(2), 173. 241 | 242 | **Expert Systems** 243 | 244 | [1] Chen, H., Zhang, M., & Chen, Z. (2025). Temporal Knowledge Graph Reasoning Based on Dynamic Fusion Representation Learning. Expert Systems, 42(2), e13758. 245 | 246 | **Information Sciences** 247 | 248 | [1] Guo, J., Zhao, M., Yu, J., Yu, R., Song, J., Wang, Q., ... & Yu, M. (2025). EHPR: Learning evolutionary hierarchy perception representation based on quaternion for temporal knowledge graph completion. Information Sciences, 688, 121409. 249 | 250 | [2] Si, Y., Hu, X., Cheng, Q., Liu, X., Liu, S., & Huang, J. (2025). Coherence mode: Characterizing local graph structural information for temporal knowledge graph. Information Sciences, 686, 121357. 251 | 252 | **Engineering Applications of Artificial Intelligence** 253 | 254 | [1] Nguyen, N. T., Ngo, T., Hoang, N., & Le, T. (2025). FTPComplEx: A flexible time perspective approach to temporal knowledge graph completion. Engineering Applications of Artificial Intelligence, 139, 109717. 255 | 256 | **Journal of Intelligent Information Systems** 257 | 258 | [1] Chen, Z., & Wu, J. (2025). Temporal knowledge graph completion based on product space and contrastive learning of commonsense. Journal of Intelligent Information Systems, 1-20. 259 | 260 | **Computer Science and Information Systems** 261 | 262 | [1] Li, S., Wang, Q., Li, Z., & Zhang, L. (2025). TPBoxE: Temporal knowledge graph completion based on time probability box embedding. Computer Science and Information Systems, (00), 6-6. | 263 | 264 | ## 2024 265 | 266 | **Knowledge-Based Systems** 267 | 268 | [1] Yue, L., Ren, Y., Zeng, Y., Zhang, J., Zeng, K., Wan, J., & Zhou, M. (2024). [Complex expressional characterizations learning based on block decomposition for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0950705124002260). Knowledge-Based Systems, 111591. 269 | 270 | [2] Zhu, L., Zhang, H., & Bai, L. (2024). [Hierarchical pattern-based complex query of temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0950705123010493). Knowledge-Based Systems, 284, 111301. 271 | 272 | [3] Huang, H., Xie, L., Liu, M., Lin, J., & Shen, H. (2024). [An embedding model for temporal knowledge graphs with long and irregular intervals](https://www.sciencedirect.com/science/article/pii/S0950705124005276). Knowledge-Based Systems, 111893. 273 | 274 | [4] Guo, J., Yu, J., Zhao, M., Yu, M., Yu, R., Xu, L., ... & Li, X. (2024). [TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S095070512400902X). Knowledge-Based Systems, 112268. 275 | 276 | [5] Hu, J., Zhu, Y., Teng, F., & Li, T. (2024). [Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism](https://www.sciencedirect.com/science/article/pii/S0950705124009146). Knowledge-Based Systems, 112280. 277 | 278 | **Applied Intelligence** 279 | 280 | [1] Wang, J., Wu, R., Wu, Y., Zhang, F., Zhang, S., & Guo, K. (2024). [MPNet: temporal knowledge graph completion based on a multi-policy network](https://link.springer.com/article/10.1007/s10489-024-05320-5). Applied Intelligence, 1-17. [Github](https://github.com/Mike-RF/MPNet) 281 | 282 | [2] Ma, Q., Zhang, X., Ding, Z., Gao, C., Shang, W., Nong, Q., ... & Jin, Z. (2024). [Temporal knowledge graph reasoning based on evolutional representation and contrastive learning](https://link.springer.com/article/10.1007/s10489-024-05767-6). Applied Intelligence, 1-19. 283 | 284 | **ACM TKDD** 285 | 286 | [1] Li, X., Zhou, H., Yao, W., Li, W., Liu, B., & Lin, Y. (2024). [Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3648366). ACM Transactions on Knowledge Discovery from Data. 287 | 288 | **Information Science** 289 | 290 | [1] (THOR) Lee, Y. C., Lee, J., Lee, D., & Kim, S. W. (2024). [Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion](https://www.sciencedirect.com/science/article/abs/pii/S0020025523014421?casa_token=clrOVF8uX6sAAAAA:CbR7aGo0qkRov9ss746qvbbzKHTyqhD9jLULqbGBFigjj8pOiRq7Vd2e9_xbnLO_sZlDjU-DmLo). Information Sciences, 654, 119857. 291 | 292 | > Extended version from: Y. -C. Lee, J. Lee, D. Lee and S. -W. Kim, ["THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks,"](https://ieeexplore.ieee.org/document/10027723) 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 1035-1040, doi: 10.1109/ICDM54844.2022.00127. [Github](https://github.com/EJHyun/THOR) 293 | 294 | [2] (Joint-MTComplEx) Zhang, F., Chen, H., Shi, Y., Cheng, J., & Lin, J. (2024). [Joint framework for tensor decomposition-based temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S002002552301438X). Information Sciences, 654, 119853. 295 | 296 | [3] (DGTL) Liu, Z., Li, Z., Li, W., & Duan, L. (2024). [Deep Graph Tensor Learning for Temporal Link Prediction](https://www.sciencedirect.com/science/article/abs/pii/S0020025523016717). Information Sciences, 120085. [Github](https://github.com/xiaolaosao/DGTL) 297 | 298 | [4] (CRmod) Zhu, L., Chai, D., & Bai, L. (2024). [CRmod: Context-Aware Rule-Guided reasoning over temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0020025524002561). Information Sciences, 120343. [Github](https://github.com/DMKE-Lab/CRmod) 299 | 300 | [5] Dai, Y., Guo, W., & Eickhoff, C. (2024). [Wasserstein adversarial learning based temporal knowledge graph embedding](https://www.sciencedirect.com/science/article/pii/S002002552301647X?via%3Dihub). Information Sciences, 659, 120061. 301 | 302 | [6] Xu, X., Jia, W., Yan, L., Lu, X., Wang, C., & Ma, Z. (2024). [Spatiotemporal knowledge graph completion via diachronic and transregional word embedding](https://www.sciencedirect.com/science/article/pii/S0020025524003906). Information Sciences, 120477. 303 | 304 | [7] Guo, J., Zhao, M., Yu, J., Yu, R., Song, J., Wang, Q., ... & Yu, M. (2024). [EHPR: Learning Evolutionary Hierarchy Perception Representation based on Quaternion for Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0020025524013239). Information Sciences, 121409. 305 | 306 | [8] Si, Y., Hu, X., Cheng, Q., Liu, X., Liu, S., & Huang, J. (2025). [Coherence mode: Characterizing local graph structural information for temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0020025524012714). Information Sciences, 686, 121357. 307 | 308 | **Information Fusion** 309 | 310 | [1] (MvTuckER) Wang, H., Yang, J., Yang, L. T., Gao, Y., Ding, J., Zhou, X., & Liu, H. (2024). [MvTuckER: Multi-view knowledge graphs represention learning based on tensor tucker model](https://www.sciencedirect.com/science/article/abs/pii/S1566253524000277). Information Fusion, 102249. 311 | 312 | **Information Processing & Management** 313 | 314 | [1] (STKGR-PR) Meng, X., Bai, L., Hu, J., & Zhu, L. (2024). [Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping](https://www.sciencedirect.com/science/article/pii/S0306457323003424). Information Processing & Management, 61(2), 103605. [Github](https://github.com/DMKE-Lab/STKGR-PR) 315 | 316 | [2] Ma, J., Li, K., Zhang, F., Wang, Y., Luo, X., Li, C., & Qiao, Y. (2024). [TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion](https://www.sciencedirect.com/science/article/pii/S0306457324002073). Information Processing & Management, 61(6), 103848. 317 | 318 | [3] Bai, L., Zhang, H., An, X., & Zhu, L. (2025). [Few-shot multi-hop reasoning via reinforcement learning and path search strategy over temporal knowledge graphs](https://www.sciencedirect.com/science/article/pii/S0306457324003601). Information Processing & Management, 62(3), 104001. 319 | 320 | **Expert Systems with Applications** 321 | 322 | [1] (CDRGN-SDE) Zhang, D., Feng, W., Wu, Z., Li, G., & Ning, B. (2024). [CDRGN-SDE: Cross-Dimensional Recurrent Graph Network with neural Stochastic Differential Equation for temporal knowledge graph embedding](https://www.sciencedirect.com/science/article/pii/S095741742400160X). Expert Systems with Applications, 123295. [Github](https://github.com/zhangdddong/CDRGN-SDE) 323 | 324 | [2] (TPComplEx) Yang, J., Ying, X., Shi, Y., & Xing, B. (2024). [Tensor decompositions for temporal knowledge graph completion with time perspective](https://www.sciencedirect.com/science/article/pii/S0957417423017694). Expert Systems with Applications, 237, 121267. [Github](https://github.com/Jinfa/TPComplEx) 325 | 326 | **Frontiers of Computer Science** 327 | 328 | [1] (EvolveKG) Liu, J., Yu, Z., Guo, B., Deng, C., Fu, L., Wang, X., & Zhou, C. (2024). [EvolveKG: a general framework to learn evolving knowledge graphs](https://link.springer.com/article/10.1007/s11704-022-2467-9). Frontiers of Computer Science, 18(3), 183309. 329 | 330 | **Neural networks** 331 | 332 | [1] Shao, P., Tao, J., & Zhang, D. (2024). [Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction](https://www.sciencedirect.com/science/article/pii/S0893608024000704). Neural Networks, 106146. 333 | 334 | [2] Bai, L., Li, N., Li, G., Zhang, Z., & Zhu, L. (2024). [Embedding-based Entity Alignment of Cross-Lingual Temporal Knowledge Graphs](https://www.sciencedirect.com/science/article/pii/S0893608024000595). Neural Networks, 106143. 335 | 336 | [3] 🔥 Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., Han, J., & Pan, S. (2024). [An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0893608024001436). Neural Networks, 106219. [Github](https://github.com/mxadorable/ILR-IR) 337 | 338 | [4] Zhang, J., Sun, M., Huang, Q., & Tian, L. (2024). [PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S0893608024004404). Neural Networks, 106516. [Github](https://github.com/KcAcoZhang/PLEASING) 339 | 340 | **Engineering Applications of Artificial Intelligence** 341 | 342 | [1] Zhu, L., Zhao, W., & Bai, L. (2024). [Quadruple mention text-enhanced temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S0952197624002161). Engineering Applications of Artificial Intelligence, 133, 108058. [Github](https://github.com/DMKE-Lab/QM-mod) 343 | 344 | **Journal of Intelligent Information Systems** 345 | 346 | [1] Du, C., Li, X., & Li, Z. (2024). [Semantic-enhanced reasoning question answering over temporal knowledge graphs](https://link.springer.com/article/10.1007/s10844-024-00840-5). Journal of Intelligent Information Systems, 1-23. 347 | 348 | **Artificial Intelligence** 349 | 350 | [1] Dong, H., Wang, P., Xiao, M., Ning, Z., Wang, P., & Zhou, Y. (2024). [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://www.sciencedirect.com/science/article/pii/S0004370224000213). Artificial Intelligence, 104085. [Github](https://github.com/hhdo/TiPNN) 351 | 352 | **IEEE Transactions on Fuzzy Systems** 353 | 354 | [1] Ji, H., Yan, L., & Ma, Z. (2023). [FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation](https://ieeexplore.ieee.org/document/10198282). IEEE Transactions on Fuzzy Systems. 355 | 356 | [2] An, X., Bai, L., Zhou, L., & Song, J. (2024). [Few-shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network](https://ieeexplore.ieee.org/abstract/document/10643313/). IEEE Transactions on Fuzzy Systems. 357 | 358 | [3] Wang, C., Yan, L., & Ma, Z. (2024). [Fuzzy Event Knowledge Graph Embedding Through Event Temporal and Causal Transfer](https://ieeexplore.ieee.org/abstract/document/10646584/). IEEE Transactions on Fuzzy Systems. 359 | 360 | **Electronics** 361 | 362 | [1] 🔥 Xu, H., Bao, J., Li, H., He, C., & Chen, F. (2024). [A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion](https://www.mdpi.com/2079-9292/13/4/742). Electronics, 13(4), 742. 363 | 364 | [2] Liu, Y., Shen, Y., & Dai, Y. (2024). [Enhancing Temporal Knowledge Graph Representation with Curriculum Learning](https://www.mdpi.com/2079-9292/13/17/3397). Electronics, 13(17), 3397. 365 | 366 | **Neurocomputing** 367 | 368 | [1] He, M., Zhu, L., & Bai, L. (2024). [ConvTKG: A query-aware convolutional neural network-based embedding model for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S092523122400451X). Neurocomputing, 127680. 369 | 370 | [2] Song, J., Bai, L., An, X., & Zhou, L. (2024). [Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learning](https://www.sciencedirect.com/science/article/pii/S0925231224017909). Neurocomputing, 129019. 371 | 372 | [3] Zhu, Y., Ma, T., Sun, S., Rong, H., Bian, Y., & Huang, K. (2024). [RTA: A reinforcement learning-based temporal knowledge graph question answering model](https://www.sciencedirect.com/science/article/pii/S092523122401765X). Neurocomputing, 128994. 373 | 374 | **IEEE TKDE** 375 | 376 | [1] Zhang, F., Zhang, Z., Zhuang, F., Zhao, Y., Wang, D., & Zheng, H. (2024). [Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement](https://ieeexplore.ieee.org/abstract/document/10504973/). IEEE Transactions on Knowledge and Data Engineering. 377 | 378 | [2] Liao, L., Zheng, L., Shang, J., Li, X., & Chen, F. (2024). [ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph](https://ieeexplore.ieee.org/abstract/document/10777929/). IEEE Transactions on Knowledge and Data Engineering. 379 | 380 | **Tsinghua Science and Technology** 381 | 382 | [1] Han, Y., Lu, G., Zhang, S., Zhang, L., Zou, C., & Wen, G. (2024). [A Temporal Knowledge Graph Embedding Model Based on Variable Translation](https://ieeexplore.ieee.org/abstract/document/10517975/). Tsinghua Science and Technology, 29(5), 1554-1565. 383 | 384 | **Applied Soft Computing** 385 | 386 | [1] Bai, L., Chen, M., & Xiao, Q. (2024). Multi-Hop Temporal Knowledge Graph Reasoning with Multi-Agent Reinforcement Learning. Applied Soft Computing, 111727. [Github](https://github.com/DMKE-Lab/MA-TPath) 387 | 388 | **IEEE Transactions on Cybernetics** 389 | 390 | [1] Wang, J., Wang, B., Gao, J., Pan, S., Liu, T., Yin, B., & Gao, W. (2024). [MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10535899/). IEEE Transactions on Cybernetics. 391 | 392 | **IEEE Transaction on AI** 393 | 394 | [1] Yang, J., Huang, C., Yang, X., Yang, L. T., Gao, Y., & Liu, C. (2024). Temporal Knowledge Extrapolation Based on Fine-grained Tensor Graph Attention Network for Responsible AI. IEEE Transactions on Artificial Intelligence. 395 | 396 | **IAENG International Journal of Computer Science** 397 | 398 | [1] Huang, C., & Zhong, Y. (2024). A Novel Approach for Representing Temporal Knowledge Graphs. IAENG International Journal of Computer Science, 51(6). 399 | 400 | **Information Systems** 401 | 402 | [1] Jia, W., Ma, R., Niu, W., Yan, L., & Ma, Z. (2024). [SFTe: Temporal Knowledge Graphs Embedding for Future Interaction Prediction](https://www.sciencedirect.com/science/article/pii/S0306437924000814). Information Systems, 102423. 403 | 404 | **IEEE/ACM Transactions on Audio, Speech, and Language Processing** 405 | 406 | [1] Gao, Y., Qiao, L., Huang, Z., Kan, Z., He, Y., & Li, D. (2024). [Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph](https://ieeexplore.ieee.org/abstract/document/10771697/). IEEE/ACM Transactions on Audio, Speech, and Language Processing. 407 | 408 | ## 2023 409 | 410 | **Semantic Web Journal** 411 | 412 | [1] (TRKGE) Song, B., Amouzouvi, K., Xu, C., Wang, M., Lehmann, J., & Vahdati, S. [Temporal Relevance for Representing Learning over Temporal Knowledge Graphs](https://www.semantic-web-journal.net/system/files/swj3557.pdf). 413 | 414 | **Expert Systems with Applications** 415 | 416 | [1] (TPRG) Bai, L., Chen, M., Zhu, L., & Meng, X. (2023). [Multi-hop temporal knowledge graph reasoning with temporal path rules guidance](https://www.sciencedirect.com/science/article/abs/pii/S0957417423003056). Expert Systems with Applications, 223, 119804. [Github](https://github.com/DMKE-Lab/TPRG) 417 | 418 | **The Journal of Supercomputing** 419 | 420 | [1] (TKGA) Wang, Z., You, X., & Lv, X. (2023). [A relation enhanced model for temporal knowledge graph alignment](https://link.springer.com/article/10.1007/s11227-023-05670-w). The Journal of Supercomputing, 1-23. 421 | 422 | **Information Systems** 423 | 424 | [1] (RITI) Liu, R., Yin, G., Liu, Z., & Tian, Y. (2023). [Reinforcement learning with time intervals for temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S030643792300128X). Information Systems, 102292. 425 | 426 | **Information Sciences** 427 | 428 | [1] (T-GAE) Hou, X., Ma, R., Yan, L., & Ma, Z. (2023). [T-GAE: A Timespan-Aware Graph Attention-based Embedding Model for Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0020025523008101). Information Sciences, 119225. 429 | 430 | [2] (TASTER) Wang, X., Lyu, S., Wang, X., Wu, X., & Chen, H. (2023). [Temporal knowledge graph embedding via sparse transfer matrix](https://www.sciencedirect.com/science/article/pii/S0020025522015122). Information Sciences, 623, 56-69. 431 | 432 | [3] (TLmod) Bai, L., Yu, W., Chai, D., Zhao, W., & Chen, M. (2023). [Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules](https://www.sciencedirect.com/science/article/pii/S0020025522013871). Information Sciences, 621, 22-35. 433 | 434 | **IEEE/ACM Transactions on Audio, Speech, and Language Processing** 435 | 436 | [1] (TARGAT) Xie, Z., Zhu, R., Liu, J., Zhou, G., & Huang, J. X. (2023). [TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding](https://ieeexplore.ieee.org/abstract/document/10141863/). IEEE/ACM Transactions on Audio, Speech, and Language Processing. 437 | 438 | **Applied Intelligence** 439 | 440 | [1] (TBDRI) Yu, M., Guo, J., Yu, J., Xu, T., Zhao, M., Liu, H., ... & Yu, R. (2023). [TBDRI: block decomposition based on relational interaction for temporal knowledge graph completion](https://link.springer.com/article/10.1007/s10489-022-03601-5). Applied Intelligence, 53(5), 5072-5084. 441 | 442 | [2] (GLANet) Wang, J., Lin, X., Huang, H., Ke, X., Wu, R., You, C., & Guo, K. (2023). [GLANet: temporal knowledge graph completion based on global and local information-aware network](https://link.springer.com/article/10.1007/s10489-023-04481-z). Applied Intelligence, 1-17. 443 | 444 | [3] (ChronoR-CP) Li, M., Sun, Z., Zhang, W., & Liu, W. (2023). [Leveraging semantic property for temporal knowledge graph completion](https://link.springer.com/article/10.1007/s10489-022-03981-8). Applied Intelligence, 53(8), 9247-9260. 445 | 446 | [4] (TIAR) Mu, C., Zhang, L., Ma, Y., & Tian, L. (2023). [Temporal knowledge subgraph inference based on time-aware relation representation](https://link.springer.com/article/10.1007/s10489-023-04833-9). Applied Intelligence, 53(20), 24237-24252. 447 | 448 | [5] (TNTSimplE) He, P., Zhou, G., Zhang, M., Wei, J., & Chen, J. (2023). [Improving temporal knowledge graph embedding using tensor factorization](https://link.springer.com/article/10.1007/s10489-021-03149-w). Applied Intelligence, 53(8), 8746-8760. 449 | 450 | **Neural Networks** 451 | 452 | [1] (TFSC) Zhang, H., & Bai, L. (2023). [Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism](https://www.sciencedirect.com/science/article/pii/S0893608023000552). Neural Networks, 161, 371-381. [Github](https://github.com/DMKE-Lab/TFSC) 453 | 454 | [2] Shao, P., Liu, T., Che, F., Zhang, D., & Tao, J. (2023). [Adaptive pseudo-Siamese policy network for temporal knowledge prediction](https://www.sciencedirect.com/science/article/pii/S0893608023000047). Neural Networks. 455 | 456 | [3] Bai, L., Han, S., & Zhu, L. (2024). [Multi-Hop Interpretable Meta Learning for Few-Shot Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0893608024009109). Neural Networks, 106981. 457 | 458 | **Neurocomputing** 459 | 460 | [1] Shao, P., He, J., Li, G., Zhang, D., & Tao, J. (2023). [Hierarchical Graph Attention Network for Temporal Knowledge Graph Reasoning](https://www.sciencedirect.com/science/article/pii/S0925231223005131). Neurocomputing, 126390. 461 | 462 | [2] (TANGO) Wang, Z., Ding, D., Ren, M., & Conti, M. (2023). [TANGO: A Temporal Spatial Dynamic Graph Model for Event Prediction](https://www.sciencedirect.com/science/article/pii/S0925231223003727). Neurocomputing, 126249. 463 | 464 | **IEEE Transactions on Neural Networks and Learning Systems** 465 | 466 | [1] (QDN) Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., & Yin, B. (2023). [QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10132432/). IEEE Transactions on Neural Networks and Learning Systems. [Github](https://github.com/jiapuwang/QDN-A-Quadruplet-Distributor-Network-for-Temporal-Knowledge-Graph-Completion) 467 | 468 | **Journal of Systems Science and Systems Engineering** 469 | 470 | [1] Yan, Z., & Tang, X. (2023). [Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph](https://link.springer.com/article/10.1007/s11518-023-5561-0). Journal of Systems Science and Systems Engineering, 32(2), 206-221. 471 | 472 | **Engineering Applications of Artificial Intelligence** 473 | 474 | [1] (RoAN) Bai, L., Ma, X., Meng, X., Ren, X., & Ke, Y. (2023). [RoAN: A relation-oriented attention network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S095219762300492X). Engineering Applications of Artificial Intelligence, 123, 106308. [Github](https://github.com/DMKE-Lab/RoAN) 475 | 476 | **Future Generation Computer Systems** 477 | 478 | [1] (TAL-TKGC) Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y., & Sun, Y. (2023). [Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0167739X23000195). Future Generation Computer Systems. 479 | 480 | **Cognitive Computation** 481 | 482 | [2] (MsCNN) Liu, W., Wang, P., Zhang, Z., & Liu, Q. (2023). [Multi-Scale Convolutional Neural Network for Temporal Knowledge Graph Completion](https://link.springer.com/article/10.1007/s12559-023-10134-7). Cognitive Computation, 1-7. 483 | 484 | **ACM Transactions on Knowledge Discovery from Data** 485 | 486 | [1] (DuCape) Zhang, S., Liang, X., Tang, H., Zheng, X., Zhang, A. X., & Ma, Y. [DuCape: Dual Quaternion and Capsule Network Based Temporal Knowledge Graph Embedding](https://dl.acm.org/doi/abs/10.1145/3589644). ACM Transactions on Knowledge Discovery from Data. 487 | 488 | **IEEE Transactions on Knowledge and Data Engineering** 489 | 490 | [1] Li, Y., Chen, H., Li, Y., Li, L., Philip, S. Y., & Xu, G. (2023). [Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation](https://arxiv.org/abs/2111.12262). IEEE Transactions on Knowledge and Data Engineering. [Github](https://github.com/Abigale001/TMER-RL) 491 | 492 | **Knowledge-Based Systems** 493 | 494 | [1] (RLAT) Bai, L., Chai, D., & Zhu, L. (2023). [RLAT: Multi-hop temporal knowledge graph reasoning based on Reinforcement Learning and Attention Mechanism](https://www.sciencedirect.com/science/article/pii/S0950705123002642). Knowledge-Based Systems, 269, 110514. 495 | 496 | [2] Luo, X., Zhu, A., Zhang, J., & Shao, J. (2024). HierarT: Multi-hop temporal knowledge graph forecasting with hierarchical reinforcement learning. Knowledge-Based Systems, 112164. 497 | 498 | **Journal of Computational Design and Engineering** 499 | 500 | [1] (MetaRT) Zhu, L., Xing, Y., Bai, L., & Chen, X. (2023). [Few-shot link prediction with meta-learning for temporal knowledge graphs](https://academic.oup.com/jcde/article-abstract/10/2/711/7069330). Journal of Computational Design and Engineering, 10(2), 711-721. 501 | 502 | **Entropy** 503 | 504 | [1] 🔥 (IMF) Du, Z., Qu, L., Liang, Z., Huang, K., Cui, L., & Gao, Z. (2023). [IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs](https://www.mdpi.com/1099-4300/25/4/666). Entropy, 25(4), 666. [Github](https://github.com/lfxx123/TKBC) 505 | 506 | **Complex & Intelligent Systems** 507 | 508 | [1] (FTMO) Zhu, L., Bai, L., Han, S., & Zhang, M. (2023). [Few-shot temporal knowledge graph completion based on meta-optimization](https://link.springer.com/article/10.1007/s40747-023-01146-9). Complex & Intelligent Systems, 9(6), 7461-7474. [Github](https://github.com/DMKE-Lab/FTMO) 509 | 510 | **World Wide Web** 511 | 512 | [1] (FTMF) Bai, L., Zhang, M., Zhang, H., & Zhang, H. (2023). [FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism](https://link.springer.com/article/10.1007/s11280-022-01091-6). World Wide Web, 26(3), 1243-1270. [Github](https://github.com/DMKE-Lab/FTMF) 513 | 514 | **DMKD** 515 | 516 | [1] (OSLT) Ma, R., Mei, B., Ma, Y., Zhang, H., Liu, M., & Zhao, L. (2023). [One-shot relational learning for extrapolation reasoning on temporal knowledge graphs](https://link.springer.com/article/10.1007/s10618-023-00935-7). Data Mining and Knowledge Discovery, 1-18. 517 | 518 | ## 2022 519 | 520 | **Knowledge-Based Systems** 521 | 522 | [1] (EvoExplore) Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao. ["Temporal knowledge graph representation learning with local and global evolutions"](https://www.sciencedirect.com/science/article/abs/pii/S0950705122006141?via%3Dihub). Knowledge-Based Systems 2022. [Github](https://github.com/zjs123/EvoExplore) 523 | 524 | [2] (TuckERT) Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, Tong Liu. ["Tucker decomposition-based temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0950705121010303?via%3Dihub). Knowledge Based Systems 2022. [Github](https://github.com/MaxenceGiraud/TensorTemporalKG) 525 | 526 | **Expert Systems with Applications** 527 | 528 | [1] (BTDG) Yujing Lai, Chuan Chen, Zibin Zheng, Yangqing Zhang. ["Block term decomposition with distinct time granularities for temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0957417422004511?via%3Dihub). Expert Systems with Applications 2022. [Github](https://github.com/JaneYul/BTDG) 529 | 530 | ## 2021 531 | 532 | **Applied Soft Computing** 533 | 534 | [1] (TPath) Luyi Bai, Wenting Yu, Mingzhuo Chen, Xiangnan Ma. ["Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning"](https://www.sciencedirect.com/science/article/abs/pii/S1568494621000673?via%3Dihub). Applied Soft Computing 2021. 535 | 536 | **TKDD** 537 | 538 | [1] (TPmod) Bai, L., Ma, X., Zhang, M., & Yu, W. (2021). [Tpmod: A tendency-guided prediction model for temporal knowledge graph completion](https://dl.acm.org/doi/abs/10.1145/3443687). ACM Transactions on Knowledge Discovery from Data, 15(3), 1-17. [Github](https://github.com/DMKE-Lab/TPmod) 539 | 540 | [2] (Dacha) Chen, L., Tang, X., Chen, W., Qian, Y., Li, Y., & Zhang, Y. (2021). [Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation](https://dl.acm.org/doi/abs/10.1145/3477051). ACM Transactions on Knowledge Discovery from Data (TKDD), 16(3), 1-18. 541 | 542 | ## 2020 543 | 544 | **IEEE Access** 545 | 546 | [1] (TDG2E) Tang, X., Yuan, R., Li, Q., Wang, T., Yang, H., Cai, Y., & Song, H. (2020). [Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution](https://ieeexplore.ieee.org/abstract/document/8950081/). IEEE Access, 8, 6849-6860. 547 | 548 | [2] (3DRTE) Wang, J., Zhang, W., Chen, X., Lei, J., & Lai, X. (2020). [3drte: 3d rotation embedding in temporal knowledge graph](https://ieeexplore.ieee.org/abstract/document/9253009/). IEEE Access, 8, 207515-207523. 549 | 550 | ## 2019 551 | 552 | **Journal of Web Semantics** 553 | 554 | [1] (ConT) Ma, Y., Tresp, V., & Daxberger, E. A. (2019). [Embedding models for episodic knowledge graphs](https://www.sciencedirect.com/science/article/pii/S1570826818300702). Journal of Web Semantics, 59, 100490. 555 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | remote_theme: pages-themes/cayman@v0.2.0 2 | plugins: 3 | - jekyll-remote-theme # add this line to the plugins list if you already have one 4 | relative_links: 5 | enabled: true 6 | collections: true 7 | include: 8 | - README.md 9 | title: Papers on Temporal Knowledge Graph Embedding and Reasoning 10 | description: Papers on TKGE-R -------------------------------------------------------------------------------- /backup/Conference.md: -------------------------------------------------------------------------------- 1 | [76] Xie, B., Wang, S., Ding, L., Chen, J., & Xiang, Y. (2023). [TBTC: A Temporal Knowledge Graph Reasoning Model with Bidirectional Temporal Correlation](https://www.researchsquare.com/article/rs-3299558/latest). 2 | 3 | 4 | [67] Sälzer, M., & Beddar-Wiesing, S. (2023). Time-Aware Robustness of Temporal Graph Neural Networks for Link Prediction. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Schloss Dagstuhl-Leibniz-Zentrum für Informatik. 5 | 6 | 7 | [47] Liu, X., Wu, J., Li, T., Chen, L., & Gao, Y. (2023). [Unsupervised entity alignment for temporal knowledge graphs](https://arxiv.org/pdf/2302.00796). arXiv preprint arXiv:2302.00796. [Github](https://github.com/ZJU-DAILY/DualMatch) 8 | 9 | 10 | [35] Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, and Jianxin Li. 2023. [Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning](https://aclanthology.org/2023.eacl-main.232/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3173–3184, Dubrovnik, Croatia. Association for Computational Linguistics. 11 | 12 | [34] Chau Nguyen, Tim French, Wei Liu, and Michael Stewart. 2023. [CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs](https://aclanthology.org/2023.eacl-main.127/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1728–1743, Dubrovnik, Croatia. Association for Computational Linguistics. 13 | 14 | 15 | [28] Zhao, X., Li, A., Jiang, R., Chen, K., & Peng, Z. (2023). [Householder Transformation-Based Temporal Knowledge Graph Reasoning](https://www.mdpi.com/2079-9292/12/9/2001). Electronics, 12(9), 2001. 16 | 17 | 18 | [7] Wen, H., Lin, Y., Xia, Y., Wan, H., Zimmermann, R., & Liang, Y. (2023). [DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models](https://arxiv.org/abs/2301.13629). arXiv preprint arXiv:2301.13629. 19 | 20 | 21 | 22 | -------------------------------------------------------------------------------- /backup/header.md: -------------------------------------------------------------------------------- 1 | # TKGER 2 | Some papers on Temporal Knowledge Graph Embedding and Reasoning 3 | 4 | ## Datasets 5 | 6 | | Name | #Entities | #Relations | #Timestamps | #Collections | Timestamp | Link download | 7 | |---|---|---|---|---|---|---| 8 | | ICEWS14 | 7128 | 230 | 365 | 90730 | point | https://paperswithcode.com/sota/link-prediction-on-icews14-1 | 9 | | ICEWS05-15 | 10488 | 251 | 4017 | 479329 | point | https://paperswithcode.com/sota/link-prediction-on-icews05-15-1 | 10 | | ICEWS18 | 23033 | 256 | 304 | 468558 | point | https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html | 11 | | GDELT | 500 | 20 | 366 | 3419607 | point | https://www.gdeltproject.org/ | 12 | | YAGO15k | 15403 | 32 | 169 | 138048 | interval | https://paperswithcode.com/sota/link-prediction-on-yago15k-1 | 13 | | WIKIDATA | 11153 | 96 | 328 | 150079 | interval | https://www.wikidata.org/wiki/Wikidata:Main_Page | 14 | 15 | ## [Content](#content) 16 | 17 | 18 | 19 | 20 | 21 | plugins: 22 | - jekyll-relative-links 23 | relative_links: 24 | enabled: true 25 | collections: true 26 | include: 27 | - CONTRIBUTING.md 28 | - README.md 29 | - LICENSE.md 30 | - COPYING.md 31 | - CODE_OF_CONDUCT.md 32 | - CONTRIBUTING.md 33 | - ISSUE_TEMPLATE.md 34 | - PULL_REQUEST_TEMPLATE.md 35 | -------------------------------------------------------------------------------- /backup/papers.csv: -------------------------------------------------------------------------------- 1 | category,title,publisher,year,type,link,authors,code 2 | Survey Papers,Temporal Knowledge Graph Completion: A Survey,ArXiv,2022,paper,https://arxiv.org/abs/2201.08236,"Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li.", 3 | Timestamp-included Tensor Decomposition, Tensor decomposition-based temporal knowledge graph embedding, ICTAI, 2020, paper, https://ieeexplore.ieee.org/abstract/document/9288194/,"Lin, Lifan and She, Kun", 4 | Timestamp-included Tensor Decomposition, Tensor decompositions for temporal knowledge base completion, ArXiv, 2020, paper, https://arxiv.org/abs/2004.04926, "Timothee Lacroix, Guillaume Obozinski, and Nicolas Usunier", -------------------------------------------------------------------------------- /backup/rl.md: -------------------------------------------------------------------------------- 1 | [1] DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning, Shangfei Zheng et.al, http://arxiv.org/abs/2304.03984v1 2 | 3 | [2] Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning, Zifeng Ding et.al., http://arxiv.org/abs/2304.00613v1 4 | 5 | [3] GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method, Xing Tang et.al., http://arxiv.org/abs/2302.11091v1, https://github.com/xt-55/GTRL 6 | 7 | [4] Deep Active Alignment of Knowledge Graph Entities and Schemata, Jiacheng Huang et.al., http://arxiv.org/abs/2304.04389v1, https://github.com/nju-websoft/daakg 8 | 9 | [5] Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion, Linhao Luo et.al., http://arxiv.org/abs/2304.08183v1, https://github.com/rmanluo/np-fkgc 10 | 11 | [6] Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method, Xuhui Jiang et.al., http://arxiv.org/abs/2304.03468v2 12 | 13 | [7] Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding, Yuke Hu et.al., http://arxiv.org/abs/2304.02932v1 14 | 15 | [8] Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment, Qian Li et.al., http://arxiv.org/abs/2304.01563v1 16 | 17 | [9] Logical Expressiveness of Graph Neural Network for Knowledge Graph Reasoning, Haiquan Qiu et.al., http://arxiv.org/abs/2303.12306v1 18 | -------------------------------------------------------------------------------- /backup/script.py: -------------------------------------------------------------------------------- 1 | # https://github.com/Thinklab-SJTU/awesome-ml4co/blob/master/src/generator.py 2 | 3 | import csv 4 | import os 5 | import copy 6 | 7 | abbr = {'Timestamp-included Tensor Decomposition': 'TiTD', 8 | 'Timestamp-based Transformation': 'TbT', 9 | 'Dynamic Embedding': 'DE', 10 | 'Learning from Knowledge Graph Snapshots': 'LKGS', 11 | 'Reasoning with Historical Context': 'RHC', 12 | } 13 | 14 | 15 | def md2csv(mdFile, csvFile): # From the md file to generate a csv file that contains the paper list. 16 | f = open(mdFile) 17 | line = f.readline() 18 | problem_start = False 19 | paper_list = [] 20 | category = None 21 | while line: 22 | print(line) 23 | if problem_start and "### [" in line: 24 | category = line[line.find("[") + 1: line.find("]")] 25 | if problem_start and '0' <= line[0] <= '9': 26 | new_paper = ["" for _ in range(7)] # 0 category, 1 title, 2 publisher, 3 year, 4 type, 5 link, 6 authors; 27 | new_paper[0] = category 28 | index = 1 29 | i = -1 30 | while i + 1 < len(line): 31 | i += 1 32 | if i < line.find(". **") + 4: 33 | continue 34 | new_paper[index] += line[i] 35 | if i == line.find(".**") and index == 1: # title -> publisher 36 | i += 3 37 | index += 1 38 | continue 39 | if line[i + 1] == "," and index == 2: # publisher -> year 40 | i += 2 41 | index += 1 42 | continue 43 | if line[i + 1] == "." and index == 3: # year -> type 44 | i += 3 45 | index += 1 46 | continue 47 | if line[i + 1] == "]" and index == 4: # type -> link 48 | i += 2 49 | index += 1 50 | continue 51 | if line[i + 1] == ")" and index == 5: # link->authors 52 | index += 1 53 | break 54 | assert index == 6 55 | _ = f.readline() 56 | line = f.readline() 57 | new_paper[index] = line[line.find('*') + 1:-2] 58 | paper_list.append(new_paper) 59 | 60 | if "
1. Survey
2. Approaches
" in line: 61 | problem_start = True 62 | line = f.readline() 63 | f.close() 64 | with open(csvFile, "w") as file: 65 | writer = csv.writer(file) 66 | writer.writerow(["category", "title", "publisher", "year", "type", "link", "authors"]) 67 | for paper in paper_list: 68 | writer.writerow(paper) 69 | 70 | 71 | def sort_by_time(elem): 72 | return elem[3] 73 | 74 | 75 | def csv2md(csvFile, mdFile, header): 76 | csvFile = open(csvFile, "r", encoding='utf-8') 77 | reader = csv.reader(csvFile) 78 | raw_papers = [] 79 | papers = [] 80 | for item in reader: 81 | if reader.line_num == 1: 82 | continue 83 | raw_papers.append(item) 84 | csvFile.close() 85 | 86 | classes = [] 87 | for paper in raw_papers: 88 | if ";" in paper[0]: 89 | paper_classes = paper[0].split(";") 90 | paper_classes = [cls.strip() for cls in paper_classes] 91 | else: 92 | paper_classes = [paper[0].strip()] 93 | for cls in paper_classes: 94 | if cls not in classes: 95 | classes.append(cls) 96 | 97 | for c in classes: 98 | p = [] 99 | for paper in raw_papers: 100 | if c in paper[0]: 101 | new_paper = copy.deepcopy(paper) 102 | new_paper[0] = c 103 | p.append(new_paper) 104 | p.sort(key=sort_by_time) 105 | papers = papers + p 106 | 107 | # command = "cp " + "years.md" + " " + header 108 | # os.system(command) 109 | command = "cp " + header + " " + mdFile 110 | os.system(command) 111 | with open(mdFile, "a", encoding='utf-8') as file: 112 | # write category 113 | for i in range(len(classes) // 2): 114 | name1 = classes[2 * i + 1] 115 | name_index1 = classes[2 * i + 1].replace(" ", "-").lower() 116 | file.writelines('\n') 117 | if name1 in abbr: 118 | file.writelines('\t 2.{} {} ({})\n'.format(name_index1, 2 * i + 1, name1, 119 | abbr[name1])) 120 | else: 121 | file.writelines('\t 2.{} {}\n'.format(name_index1, 2 * i + 1, name1)) 122 | if 2 * i + 1 < len(classes) - 1: 123 | name2 = classes[2 * i + 2] 124 | name_index2 = classes[2 * i + 2].replace(" ", "-").lower() 125 | if name2 in abbr: 126 | file.writelines( 127 | '\t 2.{} {} ({})\n'.format(name_index2, 2 * i + 2, name2, 128 | abbr[name2])) 129 | else: 130 | file.writelines('\t 2.{} {}\n'.format(name_index2, 2 * i + 2, name2)) 131 | else: 132 | file.writelines(' \n') 133 | file.writelines('\n') 134 | file.writelines('\n') 135 | 136 | # write content 137 | file.write('\n') 138 | file.write('\n') 139 | file.write('\n') 140 | file.write('\n') 141 | num = 0 142 | category = papers[0][0] 143 | file.writelines("### [{}](#content)".format(category)) 144 | file.write('\n') 145 | file.write('\n') 146 | for paper in papers: 147 | paper = [p.strip() for p in paper] 148 | if paper[0] != category: 149 | if category == "Survey Papers": 150 | file.writelines("## [Approaches](#content)") 151 | file.write('\n') 152 | file.write('\n') 153 | category = paper[0] 154 | file.writelines("### [{}](#content)".format(category)) 155 | file.write('\n') 156 | file.write('\n') 157 | num = 0 158 | num += 1 159 | # "category", "title", "publisher", "year", "type", "link", "authors, *code" 160 | if paper[7] == "": 161 | file.writelines( 162 | "{}. **{}** {}, {}. [{}]({})".format(num, paper[1], paper[2], paper[3], paper[4], paper[5])) 163 | else: 164 | file.writelines( 165 | "{}. **{}** {}, {}. [{}]({}), [code]({})".format(num, paper[1], paper[2], paper[3], paper[4], 166 | paper[5], paper[7])) 167 | file.write('\n') 168 | file.write('\n') 169 | file.writelines(" *{}*".format(paper[6])) 170 | file.write('\n') 171 | file.write('\n') 172 | 173 | 174 | if __name__ == '__main__': 175 | # md2csv("../README.md", "../data/papers.csv") 176 | csv2md("papers.csv", "README.md", "years.md") -------------------------------------------------------------------------------- /backup/years.md: -------------------------------------------------------------------------------- 1 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | ![Github repo stars](https://img.shields.io/github/stars/stmrdus/tkger) 3 | ![GitHub last commit](https://img.shields.io/github/last-commit/stmrdus/tkger) 4 | ![Visitor count](https://shields-io-visitor-counter.herokuapp.com/badge?page=stmrdus.tkger) 5 | 6 | GitHub top languageGitHub issues 7 | GitHub repo size 8 | GitHub last commit 9 | GitHub forks 10 | GitHub stars 11 | GitHub 12 | 13 | # TKGER 14 | Some papers on Temporal Knowledge Graph Embedding and Reasoning 15 | 16 | ## Datasets 17 | 18 | | Name | #Entities | #Relations | #Timestamps | #Collections | Timestamp | Link download | 19 | |---|---|---|---|---|---|---| 20 | | ICEWS14 | 7128 | 230 | 365 | 90730 | point | [https://paperswithcode.com/sota/link-prediction-on-icews14-1](https://paperswithcode.com/sota/link-prediction-on-icews14-1) | 21 | | ICEWS05-15 | 10488 | 251 | 4017 | 479329 | point | [https://paperswithcode.com/sota/link-prediction-on-icews05-15-1](https://paperswithcode.com/sota/link-prediction-on-icews05-15-1) | 22 | | ICEWS18 | 23033 | 256 | 304 | 468558 | point | [https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html](https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html) | 23 | | GDELT | 500 | 20 | 366 | 3419607 | point | [https://www.gdeltproject.org/](https://www.gdeltproject.org/) | 24 | | YAGO15k | 15403 | 32 | 169 | 138048 | interval | [https://paperswithcode.com/sota/link-prediction-on-yago15k-1](https://paperswithcode.com/sota/link-prediction-on-yago15k-1) | 25 | | WIKIDATA | 11153 | 96 | 328 | 150079 | interval | [https://www.wikidata.org/wiki/Wikidata:Main_Page](https://www.wikidata.org/wiki/Wikidata:Main_Page) | 26 | 27 | 28 | ### 2023 29 | 30 | [43] Shao, P., He, J., Li, G., Zhang, D., & Tao, J. (2023). [Hierarchical Graph Attention Network for Temporal Knowledge Graph Reasoning](https://www.sciencedirect.com/science/article/pii/S0925231223005131). Neurocomputing, 126390. 31 | 32 | [42] Mirtaheri, M., Rostami, M., & Galstyan, A. (2023). [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://arxiv.org/pdf/2305.18675.pdf). arXiv preprint arXiv:2305.18675. 33 | 34 | [41] Hou, X., Ma, R., Yan, L., & Ma, Z. (2023). [T-GAE: A Timespan-Aware Graph Attention-based Embedding Model for Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0020025523008101). Information Sciences, 119225. 35 | 36 | [40] Rage, U. K., Maharana, A., & Polepalli, K. R. (2023, May). [A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules](https://link.springer.com/chapter/10.1007/978-3-031-33374-3_21). In Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I (pp. 264-275). Cham: Springer Nature Switzerland. 37 | 38 | [39] Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., & Yin, B. (2023). [QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10132432/). IEEE Transactions on Neural Networks and Learning Systems. 39 | 40 | [38] Xu, W., Liu, B., Peng, M., Jia, X., & Peng, M. (2023). [Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion](https://arxiv.org/abs/2305.07912). arXiv preprint arXiv:2305.07912. 41 | 42 | [37] Yan, Z., & Tang, X. (2023). [Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph](https://link.springer.com/article/10.1007/s11518-023-5561-0). Journal of Systems Science and Systems Engineering, 32(2), 206-221. 43 | 44 | [36] Bai, L., Ma, X., Meng, X., Ren, X., & Ke, Y. (2023). [RoAN: A relation-oriented attention network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S095219762300492X). Engineering Applications of Artificial Intelligence, 123, 106308. 45 | 46 | [35] Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, and Jianxin Li. 2023. [Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning](https://aclanthology.org/2023.eacl-main.232/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3173–3184, Dubrovnik, Croatia. Association for Computational Linguistics. 47 | 48 | [34] Chau Nguyen, Tim French, Wei Liu, and Michael Stewart. 2023. [CylE: Cylinder Embeddings for Multi-hop Reasoning over Knowledge Graphs](https://aclanthology.org/2023.eacl-main.127/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1728–1743, Dubrovnik, Croatia. Association for Computational Linguistics. 49 | 50 | [33] Xin Ren, Luyi Bai, Qianwen Xiao, and Xiangxi Meng. 2023. [Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion](https://dl.acm.org/doi/abs/10.1145/3543507.3583397). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 2539–2547. https://doi.org/10.1145/3543507.3583397 51 | 52 | [32] Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, and Liang Wang. 2023. [Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3543507.3583242). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 2412–2422. https://doi.org/10.1145/3543507.3583242 53 | 54 | [31] Wang, Z., Ding, D., Ren, M., & Conti, M. (2023). [TANGO: A Temporal Spatial Dynamic Graph Model for Event Prediction](https://www.sciencedirect.com/science/article/pii/S0925231223003727). Neurocomputing, 126249. 55 | 56 | [30] Guozhen Zhang, Tian Ye, Depeng Jin, and Yong Li. 2023. [An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction](https://dl.acm.org/doi/abs/10.1145/3543507.3583396). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 429–437. https://doi.org/10.1145/3543507.3583396 57 | 58 | [29] Yu Liu, Wen Hua, Kexuan Xin, Saeid Hosseini, and Xiaofang Zhou. 2023. [TEA: Time-aware Entity Alignment in Knowledge Graphs](https://dl.acm.org/doi/abs/10.1145/3543507.3583317). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 2591–2599. https://doi.org/10.1145/3543507.3583317 59 | 60 | [28] Zhao, X., Li, A., Jiang, R., Chen, K., & Peng, Z. (2023). [Householder Transformation-Based Temporal Knowledge Graph Reasoning](https://www.mdpi.com/2079-9292/12/9/2001). Electronics, 12(9), 2001. 61 | 62 | [27] Dong, H., Ning, Z., Wang, P., Qiao, Z., Wang, P., Zhou, Y., & Fu, Y. (2023). [Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2304.12604). arXiv preprint arXiv:2304.12604. 63 | 64 | [26] Zhang, S., Liang, X., Li, Z., Feng, J., Zheng, X., & Wu, B. (2023, April). [BiQCap: A Biquaternion and Capsule Network-Based Embedding Model for Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_45). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 673-688). Cham: Springer Nature Switzerland. 65 | 66 | [25] Mo, C., Wang, Y., Jia, Y., & Luo, C. (2023, April). [Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning](https://link.springer.com/chapter/10.1007/978-981-99-1639-9_25). In Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part IV (pp. 300-312). Singapore: Springer Nature Singapore. 67 | 68 | [24] Zheng, S., Yin, H., Chen, T., Nguyen, Q. V. H., Chen, W., & Zhao, L. (2023). [DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2304.03984). arXiv preprint arXiv:2304.03984. 69 | 70 | [23] Yue, L., Ren, Y., Zeng, Y., Zhang, J., Zeng, K., & Wan, J. (2023, April). [Block Decomposition with Multi-granularity Embedding for Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_47). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 706-715). Cham: Springer Nature Switzerland. 71 | 72 | [22] Gong, X., Qin, J., Chai, H., Ding, Y., Jia, Y., & Liao, Q. (2023, April). [Temporal-Relational Matching Network for Few-Shot Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_52). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 768-783). Cham: Springer Nature Switzerland. 73 | 74 | [21] Liu, W., Wang, P., Zhang, Z., & Liu, Q. (2023). [Multi-Scale Convolutional Neural Network for Temporal Knowledge Graph Completion](https://link.springer.com/article/10.1007/s12559-023-10134-7). Cognitive Computation, 1-7. 75 | 76 | [20] Zhang, S., Liang, X., Tang, H., Zheng, X., Zhang, A. X., & Ma, Y. [DuCape: Dual Quaternion and Capsule Network Based Temporal Knowledge Graph Embedding](https://dl.acm.org/doi/abs/10.1145/3589644). ACM Transactions on Knowledge Discovery from Data. 77 | 78 | [19] Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y., & Sun, Y. (2023). [Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0167739X23000195). Future Generation Computer Systems. 79 | 80 | [18] Wang, X., Lyu, S., Wang, X., Wu, X., & Chen, H. (2023). [Temporal knowledge graph embedding via sparse transfer matrix](https://www.sciencedirect.com/science/article/pii/S0020025522015122). Information Sciences, 623, 56-69. 81 | 82 | [17] Bai, L., Yu, W., Chai, D., Zhao, W., & Chen, M. (2023). [Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules](https://www.sciencedirect.com/science/article/pii/S0020025522013871). Information Sciences, 621, 22-35. 83 | 84 | [16] Nolting, S., Han, Z., & Tresp, V. (2023). [Modeling the evolution of temporal knowledge graphs with uncertainty](https://arxiv.org/abs/2301.04977). arXiv preprint arXiv:2301.04977. 85 | 86 | [15] Gottschalk, S., Kacupaj, E., Abdollahi, S., Alves, D., Amaral, G., Koutsiana, E., ... & Thakkar, G. (2023). [Oekg: The open event knowledge graph](https://arxiv.org/abs/2302.14688). arXiv preprint arXiv:2302.14688. 87 | 88 | [14] Hou, X., Ma, R., Yan, L., & Ma, Z. (2023). [DAuCNet: deep autoregressive framework for temporal link prediction combining copy mechanism network](https://link.springer.com/article/10.1007/s10115-022-01823-0). Knowledge and Information Systems, 1-25. 89 | 90 | [13] Shao, P., Liu, T., Che, F., Zhang, D., & Tao, J. (2023). [Adaptive pseudo-Siamese policy network for temporal knowledge prediction](https://www.sciencedirect.com/science/article/pii/S0893608023000047). Neural Networks. 91 | 92 | [12] Gao, Q., Wang, W., Huang, L., Yang, X., Li, T., & Fujita, H. (2023). [Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion](https://www.sciencedirect.com/science/article/pii/S1566253522002287). Information Fusion, 92, 46-63. 93 | 94 | [11] Zhong, Y., & Huang, C. (2023). [A dynamic graph representation learning based on temporal graph transformer](https://www.sciencedirect.com/science/article/pii/S1110016822005336). Alexandria Engineering Journal, 63, 359-369. 95 | 96 | [10] Zhao, N., Long, Z., Wang, J., & Zhao, Z. D. (2023). [AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder](https://www.sciencedirect.com/science/article/pii/S0950705122011741). Knowledge-Based Systems, 259, 110078. 97 | 98 | [9] Li, Z., Yu, J., Zhang, G., & Xu, L. (2023). [Dynamic spatio-temporal graph network with adaptive propagation mechanism for multivariate time series forecasting](https://www.sciencedirect.com/science/article/pii/S0957417422023922). Expert Systems with Applications, 216, 119374. 99 | 100 | [8] Wang, H., Cai, S., Liu, P., Zhang, J., Shen, Z., & Liu, K. (2023). [DP-STGAT: Traffic statistics publishing with differential privacy and a spatial-temporal graph attention network](https://www.sciencedirect.com/science/article/pii/S0020025522013421). Information Sciences, 623, 258-274. 101 | 102 | [7] Wen, H., Lin, Y., Xia, Y., Wan, H., Zimmermann, R., & Liang, Y. (2023). [DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models](https://arxiv.org/abs/2301.13629). arXiv preprint arXiv:2301.13629. 103 | 104 | [6] Mo, X., Tang, R., & Liu, H. (2023). [A relation-aware heterogeneous graph convolutional network for relationship prediction](https://www.sciencedirect.com/science/article/pii/S0020025522015535). Information Sciences, 623, 311-323. 105 | 106 | [5] Lou, Y., Wang, C., Gu, T., Feng, H., Chen, J., & Yu, J. X. (2023). [Time-topology analysis on temporal graphs](https://link.springer.com/article/10.1007/s00778-022-00772-y). The VLDB Journal, 1-29. 107 | 108 | [4] Wang, J., Shi, Y., Yu, H., Zhang, K., Wang, X., Yan, Z., & Li, H. (2023). [Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning](https://www.sciencedirect.com/science/article/pii/S095741742201627X). Expert Systems with Applications, 211, 118563. 109 | 110 | [3] Huan, C., Song, S. L., Pandey, S., Liu, H., Liu, Y., Lepers, B., ... & Wu, Y. (2023). [TEA: A General-Purpose Temporal Graph Random Walk Engine](https://madsys.cs.tsinghua.edu.cn/publications/eurosys23-huan.pdf). 111 | 112 | [2] Huang, N., Wang, S., Wang, R., Cai, G., Liu, Y., & Dai, Q. (2023). [Gated spatial-temporal graph neural network based short-term load forecasting for wide-area multiple buses](https://www.sciencedirect.com/science/article/pii/S0142061522006470). International Journal of Electrical Power & Energy Systems, 145, 108651. 113 | 114 | [1] Li, Y., Chen, H., Li, Y., Li, L., Philip, S. Y., & Xu, G. (2023). [Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation](https://arxiv.org/abs/2111.12262). IEEE Transactions on Knowledge and Data Engineering. 115 | 116 | Chen, H., Li, Y., Sun, X., Xu, G., & Yin, H. (2021, March). [Temporal meta-path guided explainable recommendation](https://arxiv.org/abs/2101.01433). In Proceedings of the 14th ACM international conference on web search and data mining (pp. 1056-1064). 117 | 118 | ### 2022 119 | 120 | [1] (BTDG) Yujing Lai, Chuan Chen, Zibin Zheng, Yangqing Zhang. ["Block term decomposition with distinct time granularities for temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0957417422004511?via%3Dihub). Expert Systems with Applications 2022. 121 | 122 | [2] (EvoExplore) Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao. ["Temporal knowledge graph representation learning with local and global evolutions"](https://www.sciencedirect.com/science/article/abs/pii/S0950705122006141?via%3Dihub). Knowledge-Based Systems 2022. 123 | 124 | [3] (TuckERT) Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, Tong Liu. ["Tucker decomposition-based temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0950705121010303?via%3Dihub). Knowledge Based Systems 2022. 125 | 126 | [4] (BoxTE) Johannes Messner, Ralph Abboud, Ismail Ilkan Ceylan. ["Temporal Knowledge Graph Completion Using Box Embeddings"](https://ojs.aaai.org/index.php/AAAI/article/view/20746). AAAI 2022. 127 | 128 | [5] (TempoQR) Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, George Karypis. ["TempoQR: Temporal Question Reasoning over Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/20526). AAAI 2022. https://github.com/cmavro/TempoQR 129 | 130 | [6] (TLogic) Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker Tresp. ["TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/20330). AAAI 2022. [https://github.com/liu-yushan/TLogic](https://github.com/liu-yushan/TLogic) 131 | 132 | [7] (MetaTKGR) Ruijie Wang, zheng li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek Abdelzaher. ["Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs"](https://openreview.net/forum?id=1LmgISIDZJ). NeurIPS 2022. 133 | 134 | [8] (CEN) Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng. ["Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning"](https://aclanthology.org/2022.acl-short.32/). ACL 2022. [https://github.com/lee-zix/cen](https://github.com/lee-zix/cen) 135 | 136 | [9] (RotateQVS) Kai Chen, Ye Wang, Yitong Li, Aiping Li. ["RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion"](https://aclanthology.org/2022.acl-long.402/). ACL 2022. 137 | 138 | [10] (rGalT) Yifu Gao, Linhui Feng, Zhigang Kan, Yi Han, Linbo Qiao, Dongsheng Li. ["Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure"](https://www.ijcai.org/proceedings/2022/284). IJCAI 2022. 139 | 140 | [11] (TiRGN) Yujia Li, Shiliang Sun, Jing Zhao. ["TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning"](https://www.ijcai.org/proceedings/2022/299). IJCAI 2022. [https://github.com/Liyyy2122/TiRGN](https://github.com/Liyyy2122/TiRGN) 141 | 142 | [12] (ALRE-IR) Xin Mei∗, Libin Yang∗, Zuowei Jiang, Xiaoyan Cai. ["An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs"](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.493/). EMNLP 2022. 143 | 144 | [13] (TKGC-AGP) Linhai Zhang, Deyu Zhou. ["Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding"](https://aclanthology.org/2022.coling-1.416/). COLING 2022. 145 | 146 | [14] (DA-Net) Kangzheng Liu, Feng Zhao, Hongxu Chen, Yicong Li, Guandong Xu, Hai Jin. ["DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning"](https://dl.acm.org/doi/10.1145/3511808.3557280). CIKM 2022. 147 | 148 | [15] (TLT-KGE) Fuwei Zhang, Zhao Zhang, Xiang Ao, Fuzhen Zhuang, Yongjun Xu, Qing He. ["Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3511808.3557233). CIKM 2022. 149 | 150 | [16] EvoKG) Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong. ["EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs"](https://dl.acm.org/doi/10.1145/3488560.3498451). WSDM 2022. [https://github.com/NamyongPark/EvoKG](https://github.com/NamyongPark/EvoKG) 151 | 152 | [17] (ARIM-TE) Tingyi Zhang, Zhixu Li, Jiaan Wang, Jianfeng Qu, Lin Yuan, An Liu, Lei Zhao, Zhigang Chen. ["Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding"](https://link.springer.com/chapter/10.1007/978-3-031-00129-1_10). DASFAA 2022. 153 | 154 | [18] (TRHyTE) Lin Yuan, Zhixu Li, Jianfeng Qu, Tingyi Zhang, An Liu, Lei Zhao, Zhigang Chen. ["TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes"](https://link.springer.com/chapter/10.1007/978-3-031-00123-9_10). DASFAA 2022. 155 | 156 | [19] (SANe) Yancong Li, Xiaoming Zhang, Bo Zhang, Haiying Ren. ["Each Snapshot to Each Space: Space Adaptation for Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007/978-3-031-19433-7_15). ISWC 2022. 157 | 158 | [20] (ST-KGE) Mojtaba Nayyeri, Sahar Vahdati, Md Tansen Khan, Mirza Mohtashim Alam, Lisa Wenige, Andreas Behrend, Jens Lehmann. ["Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007/978-3-031-06981-9_15). ESWC 2022. 159 | 160 | ### 2021 161 | 162 | [1] (TPath) Luyi Bai, Wenting Yu, Mingzhuo Chen, Xiangnan Ma. ["Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning"](https://www.sciencedirect.com/science/article/abs/pii/S1568494621000673?via%3Dihub). Applied Soft Computing 2021. 163 | 164 | [2] (xERTE) Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp. ["Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs"](https://iclr.cc/virtual/2021/poster/3378). ICLR 2021. [https://github.com/TemporalKGTeam/xERTE](https://github.com/TemporalKGTeam/xERTE) 165 | 166 | [3] (ChronoR) Ali Sadeghian, Mohammadreza Armandpour, Anthony Colas, Daisy Zhe Wang. ["ChronoR: Rotation Based Temporal Knowledge Graph Embedding"](https://ojs.aaai.org/index.php/AAAI/article/view/16802). AAAI 2021. 167 | 168 | [4] (CyGNet) Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, Yan Zhang. ["Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks"](https://ojs.aaai.org/index.php/AAAI/article/view/16604). AAAI 2021. [https://github.com/CunchaoZ/CyGNet](https://github.com/CunchaoZ/CyGNet) 169 | 170 | [5] (NLSM) Tony Gracious, Shubham Gupta, Arun Kanthali, Rui M. Castro, Ambedkar Dukkipati. ["Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/16526). AAAI 2021. 171 | 172 | [6] (CluSTeR) Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng. ["Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs"](https://aclanthology.org/2021.acl-long.365/). ACL/IJCNLP 2021. 173 | 174 | [7] (HERCULES) Sebastien Montella, Lina Maria Rojas-Barahona, Johannes Heinecke. ["Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures"](https://aclanthology.org/2021.findings-acl.292/). ACL/IJCNLP (Findings) 2021. 175 | 176 | [8] (HIPNet) Yongquan He, Peng Zhang, Luchen Liu, Qi Liang, Wenyuan Zhang, Chuang Zhang, ["HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph"](https://www.ijcai.org/proceedings/2021/264). IJCAI 2021. [https://github.com/Yongquan-He/HIP-network](https://github.com/Yongquan-He/HIP-network) 177 | 178 | [9] (TANGO) Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, Volker Tresp. ["Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs"](https://aclanthology.org/2021.emnlp-main.658/). EMNLP 2021. 179 | 180 | [10] (TEA-GNN) Chengjin Xu, Fenglong Su, Jens Lehmann. ["Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs"](https://aclanthology.org/2021.emnlp-main.709/). EMNLP 2021. [https://github.com/soledad921/TEA-GNN](https://github.com/soledad921/TEA-GNN) 181 | 182 | [11] (TEE) Zhen Han, Gengyuan Zhang, Yunpu Ma, Volker Tresp. ["Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework"](https://aclanthology.org/2021.emnlp-main.639/). EMNLP 2021. 183 | 184 | [12] (TITer) Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, Kun He. ["TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting"](https://aclanthology.org/2021.emnlp-main.655/). EMNLP 2021. [https://github.com/JHL-HUST/TITer](https://github.com/JHL-HUST/TITer) 185 | 186 | [13] (RTFE) Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Haotian Wang, Jinrui Yang. ["RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/2021.naacl-main.451/). NAACL-HLT 2021. 187 | 188 | [14] (TeLM) Chengjin Xu, Yung-Yu Chen, Mojtaba Nayyeri, Jens Lehmann. ["Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings"](https://www.aclweb.org/anthology/2021.naacl-main.202/). NAACL-HLT 2021. [https://github.com/soledad921/TeLM](https://github.com/soledad921/TeLM) 189 | 190 | [15] (T-GAP) Jaehun Jung, Jinhong Jung, U. Kang. ["Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3447548.3467292). KDD 2021. [https://github.com/anonymoususer99/T-GAP](https://github.com/anonymoususer99/T-GAP) 191 | 192 | [16] (RE-GCN) Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, Xueqi Cheng. ["Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning"](https://dl.acm.org/doi/10.1145/3404835.3462963). SIGIR 2021. [https://github.com/Lee-zix/RE-GCN](https://github.com/Lee-zix/RE-GCN) 193 | 194 | [17] (TIE) Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung. ["TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3404835.3462961). SIGIR 2021. [https://github.com/JiapengWu/Time-Aware-Incremental-Embedding](https://github.com/JiapengWu/Time-Aware-Incremental-Embedding) 195 | 196 | [18] (DBKGE) Siyuan Liao, Shangsong Liang, Zaiqiao Meng, Qiang Zhang. ["Learning Dynamic Embeddings for Temporal Knowledge Graphs"](https://dl.acm.org/doi/10.1145/3437963.3441741). WSDM 2021. 197 | 198 | [19] (ST-ConvKB) Jiasheng Zhang, Shuang Liang, Zhiyi Deng, Jie Shao. ["Spatial-Temporal Attention Network for Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007%2F978-3-030-73194-6_15). DASFAA 2021. 199 | 200 | [20] (RETRA) Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin. ["RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings"](https://link.springer.com/chapter/10.1007%2F978-3-030-77385-4_25). ESWC 2021. [https://github.com/siwer/Retra](https://github.com/siwer/Retra) 201 | 202 | 203 | ### 2020 204 | 205 | [1] (TComplEx) Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier. ["Tensor Decompositions for Temporal Knowledge Base Completion"](https://openreview.net/forum?id=rke2P1BFwS). ICLR 2020. [https://github.com/facebookresearch/tkbc](https://github.com/facebookresearch/tkbc) 206 | 207 | [2] (DE-SimplE) Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart. ["Diachronic Embedding for Temporal Knowledge Graph Completion"](https://aaai.org/ojs/index.php/AAAI/article/view/5815). AAAI 2020. [https://github.com/BorealisAI/DE-SimplE](https://github.com/BorealisAI/DE-SimplE) 208 | 209 | [3] (DArtNet) Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren. ["Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution"](https://www.ijcai.org/Proceedings/2020/386). IJCAI 2020. [https://github.com/INK-USC/DArtNet](https://github.com/INK-USC/DArtNet) 210 | 211 | [4] (DyERNIE) Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp. ["DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/2020.emnlp-main.593/). EMNLP 2020. 212 | 213 | [5] (RE-NET) Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren. ["Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs"](https://www.aclweb.org/anthology/2020.emnlp-main.541/). EMNLP 2020. [https://github.com/INK-USC/RE-Net](https://github.com/INK-USC/RE-Net) 214 | 215 | [6] (TeMP) Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton. ["TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/2020.emnlp-main.462/). EMNLP 2020. [https://github.com/JiapengWu/TeMP](https://github.com/JiapengWu/TeMP) 216 | 217 | [7] (TIMEPLEX) Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti. ["Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols"](https://www.aclweb.org/anthology/2020.emnlp-main.305/). EMNLP 2020. [https://github.com/dair-iitd/tkbi](https://github.com/dair-iitd/tkbi) 218 | 219 | [8] (TeRo) Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann. ["TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation"](https://www.aclweb.org/anthology/2020.coling-main.139/). COLING 2020. [https://github.com/soledad921/ATISE](https://github.com/soledad921/ATISE) 220 | 221 | [9] (ToKE) Julien Leblay, Melisachew Wudage Chekol, Xin Liu. ["Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision"](https://dl.acm.org/doi/10.1145/3340531.3412028). CIKM 2020. [https://gitlab.com/jleblay/tokei](https://gitlab.com/jleblay/tokei) 222 | 223 | [10] (ATiSE) Chenjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann. ["Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding"](https://link.springer.com/chapter/10.1007%2F978-3-030-62419-4_37). ISWC 2020. [https://github.com/soledad921/ATISE](https://github.com/soledad921/ATISE) 224 | 225 | [11] (TDGNN) Liang Qu, Huaisheng Zhu, Qiqi Duan, Yuhui Shi. ["Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network"](https://dl.acm.org/doi/10.1145/3366423.3380073). WWW 2020. [https://github.com/Leo-Q-316/TDGNN](https://github.com/Leo-Q-316/TDGNN) 226 | 227 | ### 2018 228 | 229 | [1] (HyTE) Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. ["HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding"](https://www.aclweb.org/anthology/D18-1225/). EMNLP 2018. [https://github.com/malllabiisc/HyTE](https://github.com/malllabiisc/HyTE) 230 | 231 | [2] (TA-DistMult) Alberto Garcia-Duran, Sebastijan Dumančić, Mathias Niepert. ["Learning Sequence Encoders for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/D18-1516/). EMNLP 2018. 232 | 233 | 234 | ### 2017 235 | 236 | [1] (Know-Evolve) Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song. ["Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs"](http://proceedings.mlr.press/v70/trivedi17a.html). ICML 2017. 237 | 238 | ### 2016 239 | 240 | [1] (t-TransE) Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Sujian Li, Baobao Chang, Zhifang Sui. ["Encoding Temporal Information for Time-Aware Link Prediction"](https://www.aclweb.org/anthology/D16-1260/). EMNLP 2016. 241 | 242 | 243 | ### 2014 244 | 245 | [1] (CTPs) Derry Tanti Wijaya, Ndapandula Nakashole, Tom M. Mitchell. ["CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection"](https://www.aclweb.org/anthology/D14-1207/). EMNLP 2014. 246 | 247 | -------------------------------------------------------------------------------- /conferences/2014.md: -------------------------------------------------------------------------------- 1 | # 2014 2 | 3 | **EMNLP** 4 | 5 | [1] (CTPs) Wijaya, D. T., Nakashole, N., & Mitchell, T. (2014, October). [CTPs: Contextual temporal profiles for time scoping facts using state change detection](https://www.aclweb.org/anthology/D14-1207/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1930-1936). -------------------------------------------------------------------------------- /conferences/2016.md: -------------------------------------------------------------------------------- 1 | # 2016 2 | 3 | **EMNLP** 4 | 5 | [1] (t-TransE) Jiang, T., Liu, T., Ge, T., Sha, L., Li, S., Chang, B., & Sui, Z. (2016, November). [Encoding temporal information for time-aware link prediction](https://www.aclweb.org/anthology/D16-1260/). In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 2350-2354). 6 | 7 | [2] (TransE-TAE) Jiang, T., Liu, T., Ge, T., Sha, L., Chang, B., Li, S., & Sui, Z. (2016, December). [Towards time-aware knowledge graph completion](https://aclanthology.org/C16-1161.pdf). In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (pp. 1715-1724). 8 | 9 | 10 | -------------------------------------------------------------------------------- /conferences/2017.md: -------------------------------------------------------------------------------- 1 | # 2017 2 | 3 | **ICML** 4 | 5 | [1] (Know-Evolve) Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017, July). [Know-evolve: Deep temporal reasoning for dynamic knowledge graphs](http://proceedings.mlr.press/v70/trivedi17a.html). In international conference on machine learning (pp. 3462-3471). PMLR. 6 | 7 | [2] Chekol, M., Pirrò, G., Schoenfisch, J., & Stuckenschmidt, H. (2017, February). [Marrying uncertainty and time in knowledge graphs](https://ojs.aaai.org/index.php/AAAI/article/download/10495/10354). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1). -------------------------------------------------------------------------------- /conferences/2018.md: -------------------------------------------------------------------------------- 1 | # 2018 2 | 3 | **EMNLP** 4 | 5 | [1] (TTransE) Leblay, J., & Chekol, M. W. (2018, April). [Deriving validity time in knowledge graph](https://dl.acm.org/doi/abs/10.1145/3184558.3191639). In Companion Proceedings of the The Web Conference 2018 (pp. 1771-1776). 6 | 7 | [2] (HyTE) Dasgupta, S. S., Ray, S. N., & Talukdar, P. (2018). [Hyte: Hyperplane-based temporally aware knowledge graph embedding](https://www.aclweb.org/anthology/D18-1225/). In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 2001-2011). [Github](https://github.com/malllabiisc/HyTE) 8 | 9 | [3] (TA-DistMult) García-Durán, A., Dumančić, S., & Niepert, M. (2018). [Learning sequence encoders for temporal knowledge graph completion](https://www.aclweb.org/anthology/D18-1516/). arXiv preprint arXiv:1809.03202. -------------------------------------------------------------------------------- /conferences/2019.md: -------------------------------------------------------------------------------- 1 | # 2019 2 | 3 | **ICLR** 4 | 5 | [1] Jin, W., Jiang, H., Qu, M., Chen, T., Zhang, C., Szekely, P., & Ren, X. (2019). [Recurrent event network: Global structure inference over temporal knowledge graph](https://openreview.net/forum?id=SyeyF0VtDr). (Rejected from ICLR 2019) 6 | 7 | [2] (DyRep) Trivedi, R., Farajtabar, M., Biswal, P., & Zha, H. (2019, May). [Dyrep: Learning representations over dynamic graphs](https://par.nsf.gov/biblio/10099025). In International conference on learning representations. 8 | 9 | **ICTAI** 10 | 11 | [1] (Hybrid-TE) Wang, Z., & Li, X. (2019, November). Hybrid-te: Hybrid translation-based temporal knowledge graph embedding. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1446-1451). IEEE. 12 | 13 | **WISE** 14 | 15 | [1] Liu, Y., Hua, W., Xin, K., & Zhou, X. (2019). Context-aware temporal knowledge graph embedding. In Web Information Systems Engineering–WISE 2019: 20th International Conference, Hong Kong, China, November 26–30, 2019, Proceedings 20 (pp. 583-598). Springer International Publishing. -------------------------------------------------------------------------------- /conferences/2020.md: -------------------------------------------------------------------------------- 1 | # 2020 2 | 3 | **EMNLP** 4 | 5 | [1] (Temp) Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. 2020. [TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://aclanthology.org/2020.emnlp-main.462/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5730–5746, Online. Association for Computational Linguistics. 6 | 7 | > Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L. (2020). [Temp: Temporal message passing for temporal knowledge graph completion](https://arxiv.org/pdf/2010.03526). arXiv preprint arXiv:2010.03526. [Github](https://github.com/JiapengWu/TeMP) 8 | 9 | [2] (RE-NET) Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020. [Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs](https://aclanthology.org/2020.emnlp-main.541/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6669–6683, Online. Association for Computational Linguistics. 10 | 11 | > Jin, W., Qu, M., Jin, X., & Ren, X. (2019). [Recurrent event network: Autoregressive structure inference over temporal knowledge graphs](https://arxiv.org/pdf/1904.05530). arXiv preprint arXiv:1904.05530. [Github](https://github.com/INK-USC/RE-Net) 12 | 13 | [3] (DyERNIE) Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp. ["DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/2020.emnlp-main.593/). EMNLP 2020. 14 | 15 | [4] (TIMEPLEX) Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti. ["Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols"](https://www.aclweb.org/anthology/2020.emnlp-main.305/). EMNLP 2020. [Github](https://github.com/dair-iitd/tkbi) 16 | 17 | **ICLR** 18 | 19 | [1] (TComplEx) Timothée Lacroix, Guillaume Obozinski, Nicolas Usunier. ["Tensor Decompositions for Temporal Knowledge Base Completion"](https://openreview.net/forum?id=rke2P1BFwS). ICLR 2020. [Github](https://github.com/facebookresearch/tkbc) 20 | 21 | **AAAI** 22 | 23 | [1] (DE-SimplE) Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart. ["Diachronic Embedding for Temporal Knowledge Graph Completion"](https://aaai.org/ojs/index.php/AAAI/article/view/5815). AAAI 2020. [Github](https://github.com/BorealisAI/DE-SimplE) 24 | 25 | [2] (EvolveGCN) Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., ... & Leiserson, C. (2020, April). [Evolvegcn: Evolving graph convolutional networks for dynamic graphs](https://aaai.org/ojs/index.php/AAAI/article/view/5984). In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 04, pp. 5363-5370). 26 | 27 | **IJCAI** 28 | 29 | [1] (DArtNet) Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren. ["Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution"](https://www.ijcai.org/Proceedings/2020/386). IJCAI 2020. [Github](https://github.com/INK-USC/DArtNet) 30 | 31 | **COLING** 32 | 33 | [1] (TeRo) Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann. ["TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation"](https://www.aclweb.org/anthology/2020.coling-main.139/). COLING 2020. [Github](https://github.com/soledad921/ATISE) 34 | 35 | **CIKM** 36 | 37 | [1] (ToKE) Julien Leblay, Melisachew Wudage Chekol, Xin Liu. ["Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision"](https://dl.acm.org/doi/10.1145/3340531.3412028). CIKM 2020. [Github](https://gitlab.com/jleblay/tokei) 38 | 39 | **ISWC** 40 | 41 | [1] (ATiSE) Chenjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann. ["Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding"](https://link.springer.com/chapter/10.1007%2F978-3-030-62419-4_37). ISWC 2020. [Github](https://github.com/soledad921/ATISE) 42 | 43 | **WWW** 44 | 45 | [1] (TDGNN) Liang Qu, Huaisheng Zhu, Qiqi Duan, Yuhui Shi. ["Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network"](https://dl.acm.org/doi/10.1145/3366423.3380073). 2020. [Github](https://github.com/Leo-Q-316/TDGNN) 46 | 47 | **ICTAI** 48 | 49 | [1] Lin, L., & She, K. (2020, November). Tensor decomposition-based temporal knowledge graph embedding. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 969-975). IEEE. 50 | 51 | **ArXiv** 52 | 53 | [1] Jung, J., Jung, J., & Kang, U. (2020). [T-gap: Learning to walk across time for temporal knowledge graph completion](https://arxiv.org/pdf/2012.10595). arXiv preprint arXiv:2012.10595. -------------------------------------------------------------------------------- /conferences/2021.md: -------------------------------------------------------------------------------- 1 | # 2021 2 | 3 | **KDD** 4 | 5 | [1] (T-GAP) Jaehun Jung, Jinhong Jung, U. Kang. ["Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3447548.3467292). KDD 2021. [https://github.com/anonymoususer99/T-GAP](https://github.com/anonymoususer99/T-GAP) 6 | 7 | **ICLR** 8 | 9 | [1] (xERTE) Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp. ["Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs"](https://iclr.cc/virtual/2021/poster/3378). ICLR 2021. [https://github.com/TemporalKGTeam/xERTE](https://github.com/TemporalKGTeam/xERTE) 10 | 11 | **AAAI** 12 | 13 | [1] (ChronoR) Ali Sadeghian, Mohammadreza Armandpour, Anthony Colas, Daisy Zhe Wang. ["ChronoR: Rotation Based Temporal Knowledge Graph Embedding"](https://ojs.aaai.org/index.php/AAAI/article/view/16802). AAAI 2021. 14 | 15 | [2] (CyGNet) Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, Yan Zhang. ["Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks"](https://ojs.aaai.org/index.php/AAAI/article/view/16604). AAAI 2021. [Github](https://github.com/CunchaoZ/CyGNet) 16 | 17 | [3] (NLSM) Tony Gracious, Shubham Gupta, Arun Kanthali, Rui M. Castro, Ambedkar Dukkipati. ["Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/16526). AAAI 2021. 18 | 19 | **ACL/IJCNLP** 20 | 21 | [1] (CluSTeR) Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng. ["Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs"](https://aclanthology.org/2021.acl-long.365/). ACL/IJCNLP 2021. 22 | 23 | [2] (HERCULES) Sebastien Montella, Lina Maria Rojas-Barahona, Johannes Heinecke. ["Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures"](https://aclanthology.org/2021.findings-acl.292/). ACL/IJCNLP (Findings) 2021. 24 | 25 | **IJCAI** 26 | 27 | [1] (HIPNet) Yongquan He, Peng Zhang, Luchen Liu, Qi Liang, Wenyuan Zhang, Chuang Zhang, ["HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph"](https://www.ijcai.org/proceedings/2021/264). IJCAI 2021. [Github](https://github.com/Yongquan-He/HIP-network) 28 | 29 | **EMNLP** 30 | 31 | [1] (TANGO) Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, Volker Tresp. ["Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs"](https://aclanthology.org/2021.emnlp-main.658/). EMNLP 2021. 32 | 33 | [2] (TEA-GNN) Chengjin Xu, Fenglong Su, Jens Lehmann. ["Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs"](https://aclanthology.org/2021.emnlp-main.709/). EMNLP 2021. [Github](https://github.com/soledad921/TEA-GNN) 34 | 35 | [3] (TEE) Zhen Han, Gengyuan Zhang, Yunpu Ma, Volker Tresp. ["Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework"](https://aclanthology.org/2021.emnlp-main.639/). EMNLP 2021. 36 | 37 | [4] (TITer) Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, Kun He. ["TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting"](https://aclanthology.org/2021.emnlp-main.655/). EMNLP 2021. [Github](https://github.com/JHL-HUST/TITer) 38 | 39 | **NAACL-HLT** 40 | 41 | [1] (RTFE) Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Haotian Wang, Jinrui Yang. ["RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion"](https://www.aclweb.org/anthology/2021.naacl-main.451/). NAACL-HLT 2021. 42 | 43 | [2] (TeLM) Chengjin Xu, Yung-Yu Chen, Mojtaba Nayyeri, Jens Lehmann. ["Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings"](https://www.aclweb.org/anthology/2021.naacl-main.202/). NAACL-HLT 2021. [Github](https://github.com/soledad921/TeLM) 44 | 45 | **SIGIR** 46 | 47 | [1] (RE-GCN) Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, Xueqi Cheng. ["Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning"](https://dl.acm.org/doi/10.1145/3404835.3462963). SIGIR 2021. [Github](https://github.com/Lee-zix/RE-GCN) 48 | 49 | [2] (TIE) Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung. ["TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3404835.3462961). SIGIR 2021. [Github](https://github.com/JiapengWu/Time-Aware-Incremental-Embedding) 50 | 51 | **WSDM** 52 | 53 | [1] (DBKGE) Siyuan Liao, Shangsong Liang, Zaiqiao Meng, Qiang Zhang. ["Learning Dynamic Embeddings for Temporal Knowledge Graphs"](https://dl.acm.org/doi/10.1145/3437963.3441741). WSDM 2021. 54 | 55 | [2] Chen, H., Li, Y., Sun, X., Xu, G., & Yin, H. (2021, March). [Temporal meta-path guided explainable recommendation](https://arxiv.org/abs/2101.01433). In Proceedings of the 14th ACM international conference on web search and data mining (pp. 1056-1064). 56 | 57 | **DASFAA** 58 | 59 | [1] (ST-ConvKB) Jiasheng Zhang, Shuang Liang, Zhiyi Deng, Jie Shao. ["Spatial-Temporal Attention Network for Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007%2F978-3-030-73194-6_15). DASFAA 2021. 60 | 61 | **ESWC** 62 | 63 | [1] (RETRA) Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin. ["RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings"](https://link.springer.com/chapter/10.1007%2F978-3-030-77385-4_25). ESWC 2021. [Github](https://github.com/siwer/Retra) 64 | 65 | **ICFEICT** 66 | 67 | [1] Wang, T. (2021, May). [Learning Diachronic Embedding and Time-Encoding Sequences for Temporal Knowledge Graph Completion](https://dl.acm.org/doi/abs/10.1145/3474198.3478171). In International Conference on Frontiers of Electronics, Information and Computation Technologies (pp. 1-7). 68 | 69 | **CKC** 70 | 71 | [1] Chekol, M. W. (2021, December). [Tensor decomposition for link prediction in temporal knowledge graphs](https://dl.acm.org/doi/abs/10.1145/3460210.3493558). In Proceedings of the 11th on Knowledge Capture Conference (pp. 253-256). 72 | 73 | **IJCNN** 74 | 75 | [1] Wang, Z., Li, L., & Zeng, D. D. (2021, July). Time-Aware Representation Learning of Knowledge Graphs. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. 76 | 77 | **TGL Workshop** 78 | 79 | [1] Ding, Z., Ma, Y., He, B., & Tresp, V. (2021). [A simple but powerful graph encoder for temporal knowledge graph completion](https://arxiv.org/pdf/2112.07791). arXiv preprint arXiv:2112.07791. -------------------------------------------------------------------------------- /conferences/2022.md: -------------------------------------------------------------------------------- 1 | # 2022 2 | 3 | **ICDM** 4 | 5 | [1] (DHU-Net) Liu, K., Zhao, F., Xu, G., Wang, X., & Jin, H. (2022, November). [Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning](https://ieeexplore.ieee.org/abstract/document/10027745/). In 2022 IEEE International Conference on Data Mining (ICDM) (pp. 279-288). IEEE. 6 | 7 | [2] (THOR) Y. -C. Lee, J. Lee, D. Lee and S. -W. Kim, ["THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks,"](https://ieeexplore.ieee.org/document/10027723) 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 1035-1040, doi: 10.1109/ICDM54844.2022.00127. 8 | 9 | **CIKM** 10 | 11 | [1] (DA-Net) Kangzheng Liu, Feng Zhao, Hongxu Chen, Yicong Li, Guandong Xu, Hai Jin. ["DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning"](https://dl.acm.org/doi/10.1145/3511808.3557280). CIKM 2022. 12 | 13 | [2] (TLT-KGE) Fuwei Zhang, Zhao Zhang, Xiang Ao, Fuzhen Zhuang, Yongjun Xu, Qing He. ["Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion"](https://dl.acm.org/doi/10.1145/3511808.3557233). CIKM 2022. [Github](https://github.com/zhangfw123/TLT-KGE) 14 | 15 | [3] Li, D., Tan, S., Wang, Y., Funakoshi, K., & Okumura, M. (2023, October). [Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction](https://dl.acm.org/doi/pdf/10.1145/3583780.3615231). In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4059-4063). 16 | 17 | **AAAI** 18 | 19 | [1] (BoxTE) Johannes Messner, Ralph Abboud, Ismail Ilkan Ceylan. ["Temporal Knowledge Graph Completion Using Box Embeddings"](https://ojs.aaai.org/index.php/AAAI/article/view/20746). AAAI 2022. 20 | 21 | [2] (TempoQR) Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Adesoji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, George Karypis. ["TempoQR: Temporal Question Reasoning over Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/20526). AAAI 2022. https://github.com/cmavro/TempoQR 22 | 23 | [3] (TLogic) Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker Tresp. ["TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs"](https://ojs.aaai.org/index.php/AAAI/article/view/20330). AAAI 2022. [https://github.com/liu-yushan/TLogic](https://github.com/liu-yushan/TLogic) 24 | 25 | **ACL** 26 | 27 | [1] (CEN) Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng. ["Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning"](https://aclanthology.org/2022.acl-short.32/). ACL 2022. [https://github.com/lee-zix/cen](https://github.com/lee-zix/cen) 28 | 29 | [2] (RotateQVS) Kai Chen, Ye Wang, Yitong Li, Aiping Li. ["RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion"](https://aclanthology.org/2022.acl-long.402/). ACL 2022. 30 | 31 | **IJCAI** 32 | 33 | [1] (rGalT) Yifu Gao, Linhui Feng, Zhigang Kan, Yi Han, Linbo Qiao, Dongsheng Li. ["Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure"](https://www.ijcai.org/proceedings/2022/284). IJCAI 2022. 34 | 35 | [2] (TiRGN) Yujia Li, Shiliang Sun, Jing Zhao. ["TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning"](https://www.ijcai.org/proceedings/2022/299). IJCAI 2022. [https://github.com/Liyyy2122/TiRGN](https://github.com/Liyyy2122/TiRGN) 36 | 37 | **NIPS** 38 | 39 | [1] (MetaTKGR) Ruijie Wang, zheng li, Dachun Sun, Shengzhong Liu, Jinning Li, Bing Yin, Tarek Abdelzaher. ["Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs"](https://openreview.net/forum?id=1LmgISIDZJ). NeurIPS 2022. 40 | 41 | **EMNLP** 42 | 43 | [1] (ALRE-IR) Xin Mei∗, Libin Yang∗, Zuowei Jiang, Xiaoyan Cai. ["An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs"](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.493/). EMNLP 2022. 44 | 45 | [2] Zhen Wang, Haotong Du, Quanming Yao, and Xuelong Li. 2022. [Search to Pass Messages for Temporal Knowledge Graph Completion](https://aclanthology.org/2022.findings-emnlp.458/). In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6160–6172, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. 46 | > Wang, Z., Du, H., Yao, Q., & Li, X. (2022). [Search to Pass Messages for Temporal Knowledge Graph Completion](https://arxiv.org/pdf/2210.16740). arXiv preprint arXiv:2210.16740. [Github](https://github.com/striderdu/SPA) 47 | 48 | [3] Sun, H., Geng, S., Zhong, J., Hu, H., & He, K. (2022, December). [Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs](https://aclanthology.org/2022.emnlp-main.507.pdf). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 7481-7493). [Github](https://github.com/JHL-HUST/GHT) 49 | 50 | [4] Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, and Xiao-Yu Zhang. 2022. MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7230–7240, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. 51 | 52 | **COLING** 53 | 54 | [1] (TKGC-AGP) Linhai Zhang, Deyu Zhou. ["Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding"](https://aclanthology.org/2022.coling-1.416/). COLING 2022. 55 | 56 | **WSDM** 57 | 58 | [1] (EvoKG) Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong. ["EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs"](https://dl.acm.org/doi/10.1145/3488560.3498451). WSDM 2022. [https://github.com/NamyongPark/EvoKG](https://github.com/NamyongPark/EvoKG) 59 | 60 | **DASFAA** 61 | 62 | [1] (ARIM-TE) Tingyi Zhang, Zhixu Li, Jiaan Wang, Jianfeng Qu, Lin Yuan, An Liu, Lei Zhao, Zhigang Chen. ["Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding"](https://link.springer.com/chapter/10.1007/978-3-031-00129-1_10). DASFAA 2022. 63 | 64 | [2] (TRHyTE) Lin Yuan, Zhixu Li, Jianfeng Qu, Tingyi Zhang, An Liu, Lei Zhao, Zhigang Chen. ["TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal-Relational Hyperplanes"](https://link.springer.com/chapter/10.1007/978-3-031-00123-9_10). DASFAA 2022. 65 | 66 | **ISWC** 67 | 68 | [1] (SANe) Yancong Li, Xiaoming Zhang, Bo Zhang, Haiying Ren. ["Each Snapshot to Each Space: Space Adaptation for Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007/978-3-031-19433-7_15). ISWC 2022. 69 | 70 | [2] Wang, S., Cai, X., Zhang, Y., & Yuan, X. (2022, October). [Crnet: Modeling concurrent events over temporal knowledge graph](https://iswc2022.semanticweb.org/wp-content/uploads/2022/11/978-3-031-19433-7_30.pdf). In International Semantic Web Conference (pp. 516-533). Cham: Springer International Publishing. 71 | 72 | **ESWC** 73 | 74 | [1] (ST-KGE) Mojtaba Nayyeri, Sahar Vahdati, Md Tansen Khan, Mirza Mohtashim Alam, Lisa Wenige, Andreas Behrend, Jens Lehmann. ["Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion"](https://link.springer.com/chapter/10.1007/978-3-031-06981-9_15). ESWC 2022. 75 | 76 | **CAKBC** 77 | 78 | [1] Ding, Z., Wu, J., He, B., Ma, Y., Han, Z., & Tresp, V. (2022). [Few-shot inductive learning on temporal knowledge graphs using concept-aware information](https://arxiv.org/pdf/2211.08169). arXiv preprint arXiv:2211.08169. [Github](https://github.com/Jasper-Wu/FILT) 79 | 80 | **ECMLKDD** 81 | 82 | [1] Wei, H., Huang, H., Zhang, T., Shi, X., & Jin, H. (2022, September). [Enhance Temporal Knowledge Graph Completion via Time-Aware Attention Graph Convolutional Network](https://link.springer.com/chapter/10.1007/978-3-031-26390-3_8). In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 122-137). Cham: Springer International Publishing. 83 | 84 | **ICMSN** 85 | 86 | [1] Peng, C. C., Shi, X., Yu, R., Ma, C., Wu, L., & Zhang, D. (2022, December). [Multi-timescale History Modeling for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10076710/). In 2022 18th International Conference on Mobility, Sensing and Networking (MSN) (pp. 477-484). IEEE. 87 | 88 | **PRICAI** 89 | 90 | [1] (ST-Net) Zuo, Y., Zhou, Y., Liu, Z., Wu, J., & Zhan, M. (2022, November). [Learning Temporal and Spatial Embedding for Temporal Knowledge Graph Reasoning](https://link.springer.com/chapter/10.1007/978-3-031-20865-2_10). In Pacific Rim International Conference on Artificial Intelligence (pp. 127-138). Cham: Springer Nature Switzerland. 91 | 92 | **ICMLNLP** 93 | 94 | [1] (TAE) Duan, H., Jin, H., Chen, K., Du, S., Fang, T., & Huo, H. (2022, December). [An effective Time-Aware Encoder for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3578741.3578758). In Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing (pp. 81-87). -------------------------------------------------------------------------------- /conferences/2023.md: -------------------------------------------------------------------------------- 1 | # 2023 2 | 3 | **NIPS** 4 | 5 | [1] *(TFLEX) Lin, X., Xu, C., Su, F., Zhou, G., Hu, T., Li, N., ... & Luo, H. (2022). [TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph](https://arxiv.org/pdf/2205.14307). arXiv preprint arXiv:2205.14307., [Github](https://github.com/LinXueyuanStdio/ 6 | 7 | **IJCAI** 8 | 9 | [1] *(DaeMon) Dong, H., Ning, Z., Wang, P., Qiao, Z., Wang, P., Zhou, Y., & Fu, Y. (2023). [Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2304.12604). arXiv preprint arXiv:2304.12604. [Github](https://github.com/hhdo/DaeMon) 10 | 11 | **AAAI** 12 | 13 | [1] (CENET) Xu, Y., Ou, J., Xu, H., & Fu, L. (2023, June). [Temporal knowledge graph reasoning with historical contrastive learning](https://ojs.aaai.org/index.php/AAAI/article/view/25601/25373). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 4765-4773). [Github](https://github.com/xyjigsaw/CENET) 14 | 15 | [2] (LCGE) Niu, G., & Li, B. (2023, June). [Logic and Commonsense-Guided Temporal Knowledge Graph Completion](https://ojs.aaai.org/index.php/AAAI/article/download/25579/25351). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 4569-4577). [Github](https://github.com/ngl567/LCGE) 16 | 17 | [3] Xu, Y., Ou, J., Xu, H., Fu, L., Zhou, L., Wang, X., & Zhou, C. (2023). [Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation](https://arxiv.org/abs/2308.15002). arXiv preprint arXiv:2308.15002. 18 | 19 | > Extended version: [46] Xu, Y., Ou, J., Xu, H., & Fu, L. (2023, June). [Temporal knowledge graph reasoning with historical contrastive learning](https://ojs.aaai.org/index.php/AAAI/article/view/25601/25373). In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 4765-4773). [Github](https://github.com/xyjigsaw/CENET) 20 | 21 | **ICLR** 22 | 23 | [1] *(TILP) Xiong, S., Yang, Y., Fekri, F., & Kerce, J. C. (2022, September). [TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs](https://openreview.net/forum?id=_X12NmQKvX). In The Eleventh International Conference on Learning Representations. 24 | 25 | **DASFAA** 26 | 27 | [1] Gong, X., Qin, J., Chai, H., Ding, Y., Jia, Y., & Liao, Q. (2023, April). [Temporal-Relational Matching Network for Few-Shot Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_52). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 768-783). Cham: Springer Nature Switzerland. 28 | 29 | [2] Yue, L., Ren, Y., Zeng, Y., Zhang, J., Zeng, K., & Wan, J. (2023, April). [Block Decomposition with Multi-granularity Embedding for Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_47). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 706-715). Cham: Springer Nature Switzerland. 30 | 31 | [3] Zhang, S., Liang, X., Li, Z., Feng, J., Zheng, X., & Wu, B. (2023, April). [BiQCap: A Biquaternion and Capsule Network-Based Embedding Model for Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-30672-3_45). In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II (pp. 673-688). Cham: Springer Nature Switzerland. 32 | 33 | **ICONIP** 34 | 35 | [1] Mo, C., Wang, Y., Jia, Y., & Luo, C. (2023, April). [Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning](https://link.springer.com/chapter/10.1007/978-981-99-1639-9_25). In Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part IV (pp. 300-312). Singapore: Springer Nature Singapore. 36 | 37 | **PAKDD** 38 | 39 | [1] *(TRKG-Miner) Rage, U. K., Maharana, A., & Polepalli, K. R. (2023, May). [A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules](https://link.springer.com/chapter/10.1007/978-3-031-33374-3_21). In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 264-275). Cham: Springer Nature Switzerland. [Github](https://github.com/ab1nash/TRKG-Miner) 40 | 41 | **IJCNN** 42 | 43 | [1] Yu, R., Liu, T., Yu, J., Zhang, W., Zhao, Y., Yang, M., ... & Guo, J. (2023, June). [Combination of Translation and Rotation in Dual Quaternion Space for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10191552/). In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 01-08). IEEE. 44 | 45 | [2] *(MOST) Ding, Z., He, B., Wu, J., Ma, Y., Han, Z., & Tresp, V. (2023, June). [Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction](https://ieeexplore.ieee.org/abstract/document/10191619/). In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE. 46 | 47 | **ICASSP** 48 | 49 | [1] Guo, J., Chen, M., Zhang, Y., Huang, J., & Liu, Z. (2023, June). [Hierarchical Hypergraph Recurrent Attention Network for Temporal Knowledge Graph Reasoning](https://ieeexplore.ieee.org/abstract/document/10095378/). In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE. 50 | 51 | **ICKS** 52 | 53 | [1] Liang, Y., Zhao, S., Cheng, B., & Yang, H. (2023, August). [Twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-40283-8_20). In International Conference on Knowledge Science, Engineering and Management (pp. 234-246). Cham: Springer Nature Switzerland. 54 | 55 | **ICWSA** 56 | 57 | [1] Zhai, H., Cao, X., Sun, P., Shen, D., Nie, T., & Kou, Y. (2023, September). [Rule-Enhanced Evolutional Dual Graph Convolutional Network for Temporal Knowledge Graph Link Prediction](https://link.springer.com/chapter/10.1007/978-981-99-6222-8_6). In International Conference on Web Information Systems and Applications (pp. 64-75). Singapore: Springer Nature Singapore. 58 | 59 | **ICANN** 60 | 61 | [1] Li, L., Liu, W., Xiong, Z., & Wang, Y. (2023, September). [Sequence-Based Modeling for Temporal Knowledge Graph Link Prediction](https://link.springer.com/chapter/10.1007/978-3-031-44216-2_45). In International Conference on Artificial Neural Networks (pp. 550-562). Cham: Springer Nature Switzerland. 62 | 63 | [2] Li, N., E, H., Shi, L., Lin, X., Song, M., & Li, Y. (2023, September). [LorenTzE: Temporal Knowledge Graph Embedding Based on Lorentz Transformation](https://link.springer.com/chapter/10.1007/978-3-031-44223-0_38). In International Conference on Artificial Neural Networks (pp. 472-484). Cham: Springer Nature Switzerland. 64 | 65 | [3] Liu, Y., Mo, Y., Chen, Z., & Liu, H. (2023, September). [LogE-Net: Logic Evolution Network for Temporal Knowledge Graph Forecasting](https://link.springer.com/chapter/10.1007/978-3-031-44216-2_39). In International Conference on Artificial Neural Networks (pp. 472-485). Cham: Springer Nature Switzerland. 66 | 67 | [4] Hou, J., Guo, X., Liu, J., Li, J., Pan, L., & Wang, W. (2023, September). [Structure-Enhanced Graph Neural ODE Network for Temporal Link Prediction](https://link.springer.com/chapter/10.1007/978-3-031-44216-2_46). In International Conference on Artificial Neural Networks (pp. 563-575). Cham: Springer Nature Switzerland. 68 | 69 | **ECMLDD** 70 | 71 | [1] Gastinger, J., Sztyler, T., Sharma, L., Schuelke, A., & Stuckenschmidt, H. (2023, September). [Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graph Forecasting](https://link.springer.com/chapter/10.1007/978-3-031-43418-1_32). In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 533-549). Cham: Springer Nature Switzerland. [Github](https://github.com/nec-research/TKG-Forecasting-Evaluation) 72 | 73 | **ICNLPCC** 74 | 75 | [1] Wang, Y., Li, L., Jian, M., Zhang, Y., & Ouyang, X. (2023, October). [A Novel Semantic-Enhanced Time-Aware Model for Temporal Knowledge Graph Completion](https://link.springer.com/chapter/10.1007/978-3-031-44696-2_12). In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 148-160). Cham: Springer Nature Switzerland. 76 | 77 | **SIGIR** 78 | 79 | [1] *(DREAM) Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, and Lei Zhao. 2023. [DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3539618.3591671). In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 1578–1588. https://doi.org/10.1145/3539618.3591671 80 | > Zheng, S., Yin, H., Chen, T., Nguyen, Q. V. H., Chen, W., & Zhao, L. (2023). [DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2304.03984). arXiv preprint arXiv:2304.03984. 81 | 82 | [2] *(StreamE) Zhang, J., Shao, J., & Cui, B. (2023, July). [StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios](https://dl.acm.org/doi/abs/10.1145/3539618.3591772). In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 622-631). 83 | 84 | [3] *(RPC) Liang, K., Meng, L., Liu, M., Liu, Y., Tu, W., Wang, S., ... & Liu, X. (2023, July). [Learn from relational correlations and periodic events for temporal knowledge graph reasoning](https://dl.acm.org/doi/abs/10.1145/3539618.3591711). In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1559-1568). 85 | 86 | **WWW** 87 | 88 | [1] *(HGLS) Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, and Liang Wang. 2023. [Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3543507.3583242). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 2412–2422. https://doi.org/10.1145/3543507.3583242, [Github](https://github.com/CRIPAC-DIG/HGLS) 89 | 90 | [2] Xin Ren, Luyi Bai, Qianwen Xiao, and Xiangxi Meng. 2023. [Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion](https://dl.acm.org/doi/abs/10.1145/3543507.3583397). In Proceedings of the ACM Web Conference 2023 (WWW '23). Association for Computing Machinery, New York, NY, USA, 2539–2547. https://doi.org/10.1145/3543507.3583397 91 | 92 | [3] *(HyIE) Zhang, S., Liang, X., Tang, H., & Guan, Z. (2023, October). [Hybrid Interaction Temporal Knowledge Graph Embedding Based on Householder Transformations](https://dl.acm.org/doi/10.1145/3581783.3613446). In Proceedings of the 31st ACM International Conference on Multimedia (pp. 8954-8962). 93 | 94 | **ICMM** 95 | 96 | [1] Zhang, S., Liang, X., Tang, H., & Guan, Z. (2023, October). [Hybrid Interaction Temporal Knowledge Graph Embedding Based on Householder Transformations](https://dl.acm.org/doi/abs/10.1145/3581783.3613446). In Proceedings of the 31st ACM International Conference on Multimedia (pp. 8954-8962). 97 | 98 | **ACL** 99 | 100 | [1] Xu, W., Liu, B., Peng, M., Jia, X., & Peng, M. (2023). [Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion](https://arxiv.org/abs/2305.07912). arXiv preprint arXiv:2305.07912., [Github](https://github.com/JaySaligia/PPT) 101 | 102 | [2] Li, J., Su, X., & Gao, G. (2023, July). [TeAST: Temporal Knowledge Graph Embedding via Archimedean Spiral Timeline](https://aclanthology.org/2023.acl-long.862.pdf). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 15460-15474). [Github](https://github.com/dellixx/TeAST) 103 | 104 | [3] Lin, Q., Liu, J., Mao, R., Xu, F., & Cambria, E. (2023, July). [TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning](https://aclanthology.org/2023.acl-long.71.pdf). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1281-1293). 105 | 106 | **ACL-EMNLP** 107 | 108 | [1] (Re-Temp) Kunze Wang, Caren Han, and Josiah Poon. 2023. [Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion](https://aclanthology.org/2023.findings-emnlp.20/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 258–269, Singapore. Association for Computational Linguistics. 109 | 110 | [2] Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Saiping Guan, Yutao Zeng, Jiafeng Guo, and Xueqi Cheng. 2023. [Temporal Knowledge Graph Reasoning Based on N-tuple Modeling](https://aclanthology.org/2023.findings-emnlp.77/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1090–1100, Singapore. Association for Computational Linguistics. 111 | 112 | [3] Zhengtao Liu, Lei Tan, Mengfan Li, Yao Wan, Hai Jin, and Xuanhua Shi. 2023. [SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning](https://aclanthology.org/2023.findings-emnlp.249/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3825–3836, Singapore. Association for Computational Linguistics. 113 | 114 | [4] Zhongwu Chen, Chengjin Xu, Fenglong Su, Zhen Huang, and Yong Dou. 2023. [Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning](https://aclanthology.org/2023.findings-emnlp.448/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6736–6746, Singapore. Association for Computational Linguistics. 115 | 116 | [5] Ningyuan Li, Haihong E, Shi Li, Mingzhi Sun, Tianyu Yao, Meina Song, Yong Wang, and Haoran Luo. 2023. [TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy](https://aclanthology.org/2023.findings-emnlp.529/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7885–7894, Singapore. Association for Computational Linguistics. [Github](https://github.com/JasonLee-22/TR-Rules) 117 | 118 | **ICWS** 119 | 120 | [1] *(TKGF-NTP) Han, G., Chen, W., Zhang, X., Xu, J., Liu, A., & Zhao, L. (2023, July). [TKGF-NTP: Temporal Knowledge Graph Forecasting via Neural Temporal Point Process](https://ieeexplore.ieee.org/abstract/document/10248330/). In 2023 IEEE International Conference on Web Services (ICWS) (pp. 318-328). IEEE. 121 | 122 | **CCAI** 123 | 124 | [1] Hu, S., Wang, B., Wang, J., Ma, Y., & Zhao, L. (2023, May). Transformer-based Temporal Knowledge Graph Completion. In 2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) (pp. 443-448). IEEE. 125 | 126 | **ICDE** 127 | 128 | [1] *(RETIA) Liu, K., Zhao, F., Xu, G., Wang, X., & Jin, H. (2023, November). [RETIA: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation](https://opus.lib.uts.edu.au/bitstream/10453/166395/3/RETIA%20relation-entity%20twin-interact%20aggregation%20for%20temporal%20knowledge%20graph%20extrapolation.pdf). In IEEE International Conference on Data Engineering. IEEE. 129 | 130 | **TGL Workshop** 131 | 132 | [1] Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2023, November). [Do Temporal Knowledge Graph Embedding Models Learn or Memorize](https://openreview.net/forum?id=UMokRwWfLW). In Temporal Graph Learning Workshop@ NeurIPS 2023. 133 | 134 | **ArXiv** 135 | 136 | [1] Nolting, S., Han, Z., & Tresp, V. (2023). [Modeling the evolution of temporal knowledge graphs with uncertainty](https://arxiv.org/abs/2301.04977). arXiv preprint arXiv:2301.04977. 137 | 138 | [2] Mirtaheri, M., Rostami, M., & Galstyan, A. (2023). [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://arxiv.org/pdf/2305.18675.pdf). arXiv preprint arXiv:2305.18675. 139 | 140 | [3] Chen, Z., Xu, C., Su, F., Huang, Z., & Dou, Y. (2023). [Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph](https://arxiv.org/abs/2302.05640). arXiv preprint arXiv:2302.05640. 141 | 142 | [4] Tang, X., & Chen, L. (2023). [GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method](https://arxiv.org/pdf/2302.11091). arXiv preprint arXiv:2302.11091. 143 | 144 | [5] Dong, H., Wang, P., Xiao, M., Ning, Z., Wang, P., & Zhou, Y. (2023). [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2309.03251). arXiv preprint arXiv:2309.03251. 145 | 146 | [6] Wang, K., Han, S. C., & Poon, J. (2023). [Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion](https://arxiv.org/abs/2310.15722). arXiv preprint arXiv:2310.15722. -------------------------------------------------------------------------------- /conferences/2024.md: -------------------------------------------------------------------------------- 1 | # 2024 2 | 3 | ## Conferences ❄️ ❄️ ❄️ 4 | 5 | **IJICAI** 6 | 7 | [1] Gastinger, J., Meilicke, C., Errica, F., Sztyler, T., Schuelke, A., & Stuckenschmidt, H. (2024). [History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting](https://arxiv.org/abs/2404.16726). arXiv preprint arXiv:2404.16726. 8 | 9 | [2] Shang, Z., Wang, P., Ke, W., Liu, J., Huang, H., Li, G., ... & Li, Y. Learning Multi-Granularity and Adaptive Representation for Knowledge Graph Reasoning. 10 | 11 | **ICLR** 12 | 13 | [1] Yin, H., Wang, Z., & Song, Y. (2023, October). [Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors](https://openreview.net/forum?id=1BmveEMNbG). In The Twelfth International Conference on Learning Representations. 14 | 15 | [2] Galkin, M., Yuan, X., Mostafa, H., Tang, J., & Zhu, Z. (2023). [Towards foundation models for knowledge graph reasoning](https://arxiv.org/abs/2310.04562). arXiv preprint arXiv:2310.04562. 16 | 17 | [3] Jiang, P., Xiao, C., Cross, A. R., & Sun, J. (2023, October). [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://openreview.net/forum?id=tVTN7Zs0ml). In The Twelfth International Conference on Learning Representations. 18 | 19 | **WSDM** 20 | 21 | [1] Hu, H., Guo, W., Liu, X., Liu, Y., Tang, R., Zhang, R., & Kan, M. Y. (2024). User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation. 22 | 23 | [2] Jiang, Y., Yang, Y., Xia, L., & Huang, C. (2024, March). DiffKG: Knowledge Graph Diffusion Model for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 313-321). 24 | 25 | [3] Liu, X., Yang, L., Liu, Z., Yang, M., Wang, C., Peng, H., & Yu, P. S. (2024, March). Knowledge Graph Context-Enhanced Diversified Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 462-471). 26 | 27 | [4] Liu, Y., Cao, Y., Wang, S., Wang, Q., & Bi, G. (2024, March). Generative Models for Complex Logical Reasoning over Knowledge Graphs. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 492-500). 28 | 29 | **AAAI** 30 | 31 | [1] Chen, Z., Zhang, D., Feng, S., Chen, K., Chen, L., Han, P., & Shang, S. (2024, March). KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 8311-8319). 32 | 33 | [2] Liu, R., Wu, L., & Zhang, P. (2024, March). KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 8805-8814). 34 | 35 | [3] Long, X., Zhuang, L., Li, A., Wei, J., Li, H., & Wang, S. (2024, March). KGDM: A Diffusion Model to Capture Multiple Relation Semantics for Knowledge Graph Embedding. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 8850-8858). 36 | 37 | [4] Shang, B., Zhao, Y., Liu, J., & Wang, D. (2024, March). LAFA: Multimodal Knowledge Graph Completion with Link Aware Fusion and Aggregation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 8957-8965). 38 | 39 | [5] Shang, B., Zhao, Y., Liu, J., & Wang, D. (2024, March). Mixed Geometry Message and Trainable Convolutional Attention Network for Knowledge Graph Completion. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 8966-8974). 40 | 41 | [6] Fan, C., Chen, Y., Xue, J., Kong, Y., Tao, J., & Lv, Z. (2024). Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion. arXiv preprint arXiv:2401.12997. 42 | 43 | [7] Su, Z., Wang, D., Miao, C., & Cui, L. (2024, March). Anchoring Path for Inductive Relation Prediction in Knowledge Graphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 8, pp. 9011-9018). 44 | 45 | [8] Liang, K., Meng, L., Zhou, S., Tu, W., Wang, S., Liu, Y., ... & Liu, X. (2024, March). MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 9, pp. 10645-10653). 46 | 47 | [9] Liang, M., Du, J., Liang, Z., Xing, Y., Huang, W., & Xue, Z. (2024, March). Self-Supervised Multi-Modal Knowledge Graph Contrastive Hashing for Cross-Modal Search. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 12, pp. 13744-13753). 48 | 49 | [10] Wu, D., Sun, W., He, Y., Chen, Z., & Luo, X. (2024, March). MKG-FENN: A Multimodal Knowledge Graph Fused End-to-End Neural Network for Accurate Drug–Drug Interaction Prediction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 9, pp. 10216-10224). 50 | 51 | [11] Xiong, S., Yang, Y., Payani, A., Kerce, J. C., & Fekri, F. (2024, March). Teilp: Time prediction over knowledge graphs via logical reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 14, pp. 16112-16119). 52 | 53 | 54 | **ICDE2024** 55 | 56 | [1] Chen, W., Wan, H., Wu, Y., Zhao, S., Cheng, J., Li, Y., & Lin, Y. (2023). Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2312.01601. 57 | 58 | **ICASSP** 59 | 60 | [1] Huang, S., Wang, Y., Chen, K., & Jia, Y. (2024, April). Temporal Relational Context Learning for Extrapolation Reasoning on Temporal Knowledge Graphs. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6430-6434). IEEE. 61 | 62 | [2] Zhang, S., Liang, X., Niu, S., Feng, J., Feng, C., & Wang, M. (2024, April). [Temporal Knowledge Graph Embedding using Householder Transformations](https://ieeexplore.ieee.org/abstract/document/10447572). In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 10106-10110). IEEE. 63 | 64 | **NIPS** 65 | 66 | [1] Shengyuan, C., Cai, Y., Fang, H., Huang, X., & Sun, M. (2024). Differentiable neuro-symbolic reasoning on large-scale knowledge graphs. Advances in Neural Information Processing Systems, 36. 67 | 68 | [2] Zhu, Z., Yuan, X., Galkin, M., Xhonneux, L. P., Zhang, M., Gazeau, M., & Tang, J. (2024). A* net: A scalable path-based reasoning approach for knowledge graphs. Advances in Neural Information Processing Systems, 36. 69 | 70 | [3] Lin, X., Xu, C., Zhou, G., Luo, H., Hu, T., Su, F., ... & Sun, M. (2024). TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph. Advances in Neural Information Processing Systems, 36. 71 | 72 | [4] Bai, J., Liu, X., Wang, W., Luo, C., & Song, Y. (2024). Complex query answering on eventuality knowledge graph with implicit logical constraints. Advances in Neural Information Processing Systems, 36. 73 | 74 | **[LREC-COLING](https://aclanthology.org/volumes/2024.lrec-main/)** 75 | 76 | [1] Turki, H., Owodunni, A. T., Taieb, M. A. H., Bile, R. F., Aouicha, M. B., & Zouhar, V. (2023). A Decade of Scholarly Research on Open Knowledge Graphs. arXiv preprint arXiv:2306.13186. 77 | 78 | [2] Fang, Z., Qin, J., Zhu, X., Yang, C., & Yin, X. C. (2024). Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding. arXiv preprint arXiv:2405.00358. 79 | 80 | [3] Wasi, A. T., Rafi, T. H., Islam, R., & Chae, D. K. (2024). BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering. arXiv preprint arXiv:2404.03528. 81 | 82 | [4] Zhang, Y., Qian, X., Zhao, Y., Zhou, B., Song, K., & Yuan, X. (2024, May). Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 2526-2536). 83 | 84 | [5] Wei, X., Chen, Y., Cheng, N., Cui, X., Xu, J., & Han, W. (2023). CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction. arXiv preprint arXiv:2307.00769. 85 | 86 | [6] Mousavi, A., Zhan, X., Bai, H., Shi, P., Rekatsinas, T., Han, B., ... & Jaitly, N. (2023). Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation. arXiv preprint arXiv:2309.11669. 87 | 88 | [7] Winter, B., Rosero, A. G. F., Loeser, A., Gers, F. A., Rosero, N. K. F., & Krestel, R. (2024, May). DDxGym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 4438-4448). 89 | 90 | [8] Dong, Y., Kong, Q., Wang, L., & Luo, Y. (2024, May). Dual Complex Number Knowledge Graph Embeddings. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 5391-5400). 91 | 92 | [9] Sun, K., Jedema, N. P., Sharma, K., Janssen, R., Pujara, J., Szekely, P., & Moschitti, A. (2024, May). Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 5585-5595). 93 | 94 | [10] Sawczyn, A., Binkowski, J., Bielak, P., & Kajdanowicz, T. (2024, May). Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 5768-5782). 95 | 96 | [11] Jiayang, C., Qiu, L., Chan, C., Liu, X., Song, Y., & Zhang, Z. (2024). EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs. arXiv preprint arXiv:2404.00209. 97 | 98 | [12] Li, Y., Zhang, X., Cui, Y., & Ma, S. (2024, May). Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 8474-8486). 99 | 100 | [13] Kim, Y., Rome, S., Foley, K., Nankani, M., Melamed, R., Morales, J., ... & Huang, H. H. (2024). Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users. arXiv preprint arXiv:2403.18667. 101 | 102 | [14] Anil, A., Gutiérrez-Basulto, V., Ibañéz-García, Y., & Schockaert, S. (2023). Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis. arXiv preprint arXiv:2308.07942. 103 | 104 | [15] Wang, Y., Hu, M., Huang, Z., Li, D., Yang, D., & Lu, X. (2024). KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion. arXiv preprint arXiv:2403.17532. 105 | 106 | [16] Zhang, S., Cao, B., & Fan, J. (2024, May). KCL: Few-shot Named Entity Recognition with Knowledge Graph and Contrastive Learning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 9681-9692). 107 | 108 | [17] Amouzouvi, K., Song, B., Vahdati, S., & Lehmann, J. (2024, May). Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 9832-9842). 109 | 110 | [18] Dougrez-Lewis, J., Kochkina, E., Liakata, M., & He, Y. (2024, May). Knowledge Graphs for Real-World Rumour Verification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 9843-9853). 111 | 112 | [19] Xu, D., Zhang, Z., Lin, Z., Wu, X., Zhu, Z., Xu, T., ... & Chen, E. (2024). Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models. arXiv preprint arXiv:2403.01972. 113 | 114 | [20] Luo, R., Li, J., Zhang, J., Xiao, J., & Yang, Y. (2024, May). Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 13014-13025). 115 | 116 | [21] Zong, L., Xie, Z., Ma, C., Liu, X., Zhang, X., & Xu, B. (2024, May). RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 13919-13928). 117 | 118 | [22] Kruit, B., Xu, Y., & Kalo, J. C. (2024, May). Retrieval-based Question Answering with Passage Expansion Using a Knowledge Graph. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14063-14072). 119 | 120 | [23] Hou, Z., Jin, X., Li, Z., Bai, L., Guo, J., & Cheng, X. (2024). Selective Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2404.01695. 121 | 122 | [24] Chen, Z., Zhang, Z., Li, Z., Wang, F., Zeng, Y., Jin, X., & Xu, Y. (2024). Self-Improvement Programming for Temporal Knowledge Graph Question Answering. arXiv preprint arXiv:2404.01720. 123 | 124 | [25] Xu, H., Wang, Y., & Fan, J. (2024, May). Self-Knowledge Distillation for Knowledge Graph Embedding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14595-14605). 125 | 126 | [26] Li, X., Zhou, H., Yao, W., Li, W., Lin, Y., & Du, L. (2024, May). Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14744-14754). 127 | 128 | [27] Liu, X., Zhang, J., Ma, C., Liang, W., Xu, B., & Zong, L. (2024, May). Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 15742-15751). 129 | 130 | [28] Li, J., Su, X., Zhang, F., & Gao, G. (2024, May). TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 16727-16737). 131 | 132 | [29] Zhang, Y., Chen, Z., Liang, L., Chen, H., & Zhang, W. (2024). Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion. arXiv preprint arXiv:2402.15444. 133 | 134 | **WWW** 135 | 136 | [1] Kouagou, N. D. J., Demir, C., Zahera, H. M., Wilke, A., Heindorf, S., Li, J., & Ngonga Ngomo, A. C. (2024, May). Universal Knowledge Graph Embeddings. In Companion Proceedings of the ACM on Web Conference 2024 (pp. 1793-1797). 137 | 138 | [2] Zhou, E., Guo, S., Ma, Z., Hong, Z., Guo, T., & Dong, P. (2024, May). Poisoning Attack on Federated Knowledge Graph Embedding. In Proceedings of the ACM on Web Conference 2024 (pp. 1998-2008). 139 | 140 | [3] Egger, M. K., Ma, W., Mottin, D., Karras, P., Bordino, I., Gullo, F., & Anagnostopoulos, A. (2024, May). ReliK: A Reliability Measure for Knowledge Graph Embeddings. In Proceedings of the ACM on Web Conference 2024 (pp. 2009-2019). 141 | 142 | [4] Long, X., Zhuang, L., Li, A., Li, H., & Wang, S. (2024, May). Fact Embedding through Diffusion Model for Knowledge Graph Completion. In Proceedings of the ACM on Web Conference 2024 (pp. 2020-2029). 143 | 144 | [5] Zhang, H., Zhang, J., & Molybog, I. (2024, May). HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding. In Proceedings of the ACM on Web Conference 2024 (pp. 2116-2127). 145 | 146 | [6] Wang, Y., Javari, A., Balaji, J., Shalaby, W., Derr, T., & Cui, X. (2024, May). Knowledge Graph-based Session Recommendation with Session-Adaptive Propagation. In Companion Proceedings of the ACM on Web Conference 2024 (pp. 264-273). 147 | 148 | [7] Zhang, X. S., Guan, W., Lu, J., Qiu, Z., Cheng, J., Wu, X., & Zheng, Y. (2024, May). GraphLeak: Patient Record Leakage through Gradients with Knowledge Graph. In Proceedings of the ACM on Web Conference 2024 (pp. 4706-4716). 149 | 150 | **NAACL** 151 | 152 | [1] Ding, Z., Cai, H., Wu, J., Ma, Y., Liao, R., Xiong, B., & Tresp, V. (2024, June). zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 1877-1895). 153 | 154 | [2] Liao, R., Jia, X., Li, Y., Ma, Y., & Tresp, V. (2024, June). GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024 (pp. 4303-4317). 155 | 156 | **International Conference on Pattern Recognition** 157 | 158 | [1] Wang, F., Zhu, G., Hou, H., Yuan, C., & Huang, Y. (2024, November). Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning. In International Conference on Pattern Recognition (pp. 227-242). Cham: Springer Nature Switzerland. 159 | 160 | **Other** 161 | 162 | [1] Jia, N., & Yao, C. (2024). ShallowBKGC: a BERT-enhanced shallow neural network model for knowledge graph completion. PeerJ Computer Science, 10, e2058. 163 | 164 | [2] He, P., Xiao, Y., He, C., & Duan, L. (2024, August). EvoREG: Evolutional Modeling with Relation-Entity Dual-Guidance for Temporal Knowledge Graph Reasoning. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (pp. 256-271). Singapore: Springer Nature Singapore. 165 | 166 | 167 | ## ArXiv 168 | 169 | [1] Ma, Y., Ye, C., Wu, Z., Wang, X., Cao, Y., Pang, L., & Chua, T. S. (2023). [Structured, Complex and Time-complete Temporal Event Forecasting](https://arxiv.org/abs/2312.01052). arXiv preprint arXiv:2312.01052. [Github](https://github.com/yecchen/GDELT-ComplexEvent) 170 | 171 | [2] Zhang, J., Hui, B., Mu, C., & Tian, L. (2023). [Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2312.03004). arXiv preprint arXiv:2312.03004. 172 | 173 | [3] Luo, R., Gu, T., Li, H., Li, J., Lin, Z., Li, J., & Yang, Y. (2024). [Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion](https://arxiv.org/abs/2401.06072). arXiv preprint arXiv:2401.06072. 174 | 175 | [4] Peng, M., Liu, B., Xu, W., Jiang, Z., Zhu, J., & Peng, M. (2024). [Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2404.00051). arXiv preprint arXiv:2404.00051. 176 | 177 | [5] Lv, A., Huang, Y., Ouyang, G., Chen, Y., & Xie, H. (2024). [RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2404.00586). arXiv preprint arXiv:2404.00586. 178 | 179 | [6] Wang, J., Cui, Z., Wang, B., Pan, S., Gao, J., Yin, B., & Gao, W. (2024). [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://arxiv.org/abs/2403.19881). arXiv preprint arXiv:2403.19881. 180 | 181 | [7] Hou, Z., Jin, X., Li, Z., Bai, L., Guo, J., & Cheng, X. (2024). [Selective Temporal Knowledge Graph Reasoning](https://arxiv.org/abs/2404.01695). arXiv preprint arXiv:2404.01695. 182 | 183 | [8] Bronzini, M., Nicolini, C., Lepri, B., Staiano, J., & Passerini, A. (2024). [Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph](https://arxiv.org/abs/2404.03623). arXiv preprint arXiv:2404.03623. 184 | 185 | [9] Maheshwari, P., Ren, H., Wang, Y., Sosic, R., & Leskovec, J. (2024). [TimeGraphs: Graph-based Temporal Reasoning](https://arxiv.org/abs/2401.03134). arXiv preprint arXiv:2401.03134. 186 | 187 | [10] Fang, Z., Qin, J., Zhu, X., Yang, C., & Yin, X. C. (2024). [Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding](https://arxiv.org/abs/2405.00358). arXiv preprint arXiv:2405.00358. 188 | 189 | [11] Fang, Z., Lei, S. L., Zhu, X., Yang, C., Zhang, S. X., Yin, X. C., & Qin, J. (2024). [Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph](https://arxiv.org/abs/2405.00352). arXiv preprint arXiv:2405.00352. 190 | 191 | [12] Li, C., Zheng, H., Sun, Y., Wang, C., Yu, L., Chang, C., ... & Liu, B. (2024). Enhancing multi-hop knowledge graph reasoning through reward shaping techniques. arXiv preprint arXiv:2403.05801. 192 | 193 | [13] Chen, H., Ni, Y., Zakeri, A., Zou, Z., Yun, S., Wen, F., ... & Imani, M. (2024). HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning. arXiv preprint arXiv:2403.05763. 194 | 195 | [14] Zhang, J., Hui, B., Mu, C., Sun, M., & Tian, L. (2024). Historically Relevant Event Structuring for Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2405.10621. 196 | 197 | [15] Yang, J., Wang, X., Wang, Y., Wang, J., & Wang, F. Y. (2024). AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2405.10346. 198 | 199 | [16] Li, R., Li, C., Shen, Y., Zhang, Z., & Chen, X. (2024). Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization. arXiv preprint arXiv:2405.08540. 200 | 201 | [17] Wang, J., Sun, K., Luo, L., Wei, W., Hu, Y., Liew, A. W. C., ... & Yin, B. (2024). Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2405.14170. 202 | 203 | [18] Ma, Y., Burns, O., Wang, M., Li, G., Du, N., Shafey, L. E., ... & Soltau, H. (2024). Knowledge Graph Reasoning with Self-supervised Reinforcement Learning. arXiv preprint arXiv:2405.13640. 204 | 205 | [19] Zhang, J., Wan, T., Mu, C., Lu, G., & Tian, L. (2024). Learning Granularity Representation for Temporal Knowledge Graph Completion. arXiv preprint arXiv:2408.15293. 206 | 207 | [20] Sun, J., Sheng, Y., & He, L. (2024). CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning. arXiv preprint arXiv:2408.07911. 208 | 209 | [21] Sannidhi, G., Sakhinana, S. S., & Runkana, V. (2024). Retrieval-Augmented Generation Meets Data-Driven Tabula Rasa Approach for Temporal Knowledge Graph Forecasting. arXiv preprint arXiv:2408.13273. 210 | 211 | [22] Ying, R., Hu, M., Wu, J., Xie, Y., Liu, X., Wang, Z., ... & Cheng, R. (2024). Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion. arXiv preprint arXiv:2408.06603. -------------------------------------------------------------------------------- /journal/2019.md: -------------------------------------------------------------------------------- 1 | # 2019 2 | 3 | **Journal of Web Semantics** 4 | 5 | [1] Ma, Y., Tresp, V., & Daxberger, E. A. (2019). [Embedding models for episodic knowledge graphs](https://www.sciencedirect.com/science/article/pii/S1570826818300702). Journal of Web Semantics, 59, 100490. -------------------------------------------------------------------------------- /journal/2020.md: -------------------------------------------------------------------------------- 1 | # 2020 2 | 3 | **IEEE Access** 4 | 5 | [1] Tang, X., Yuan, R., Li, Q., Wang, T., Yang, H., Cai, Y., & Song, H. (2020). [Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution](https://ieeexplore.ieee.org/abstract/document/8950081/). IEEE Access, 8, 6849-6860. 6 | 7 | [2] Wang, J., Zhang, W., Chen, X., Lei, J., & Lai, X. (2020). [3drte: 3d rotation embedding in temporal knowledge graph](https://ieeexplore.ieee.org/abstract/document/9253009/). IEEE Access, 8, 207515-207523. -------------------------------------------------------------------------------- /journal/2021.md: -------------------------------------------------------------------------------- 1 | # 2021 2 | 3 | **Applied Soft Computing** 4 | 5 | [1] (TPath) Luyi Bai, Wenting Yu, Mingzhuo Chen, Xiangnan Ma. ["Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning"](https://www.sciencedirect.com/science/article/abs/pii/S1568494621000673?via%3Dihub). Applied Soft Computing 2021. 6 | 7 | **TKDD** 8 | 9 | [1] (Tpmod) Bai, L., Ma, X., Zhang, M., & Yu, W. (2021). [Tpmod: A tendency-guided prediction model for temporal knowledge graph completion](https://dl.acm.org/doi/abs/10.1145/3443687). ACM Transactions on Knowledge Discovery from Data, 15(3), 1-17. 10 | 11 | [2] (Dacha) Chen, L., Tang, X., Chen, W., Qian, Y., Li, Y., & Zhang, Y. (2021). [Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation](https://dl.acm.org/doi/abs/10.1145/3477051). ACM Transactions on Knowledge Discovery from Data (TKDD), 16(3), 1-18. -------------------------------------------------------------------------------- /journal/2022.md: -------------------------------------------------------------------------------- 1 | # 2022 2 | 3 | **Knowledge-Based Systems** 4 | 5 | [1] (EvoExplore) Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao. ["Temporal knowledge graph representation learning with local and global evolutions"](https://www.sciencedirect.com/science/article/abs/pii/S0950705122006141?via%3Dihub). Knowledge-Based Systems 2022. 6 | 7 | [2] (TuckERT) Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, Tong Liu. ["Tucker decomposition-based temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0950705121010303?via%3Dihub). Knowledge Based Systems 2022. 8 | 9 | **Expert Systems with Applications** 10 | 11 | [1] (BTDG) Yujing Lai, Chuan Chen, Zibin Zheng, Yangqing Zhang. ["Block term decomposition with distinct time granularities for temporal knowledge graph completion"](https://www.sciencedirect.com/science/article/abs/pii/S0957417422004511?via%3Dihub). Expert Systems with Applications 2022. -------------------------------------------------------------------------------- /journal/2023.md: -------------------------------------------------------------------------------- 1 | ## 2023 2 | 3 | **Semantic Web Journal** 4 | 5 | [1] (TRKGE) Song, B., Amouzouvi, K., Xu, C., Wang, M., Lehmann, J., & Vahdati, S. [Temporal Relevance for Representing Learning over Temporal Knowledge Graphs](https://www.semantic-web-journal.net/system/files/swj3557.pdf). 6 | 7 | **Expert Systems with Applications** 8 | 9 | [1] (TPRG) Bai, L., Chen, M., Zhu, L., & Meng, X. (2023). [Multi-hop temporal knowledge graph reasoning with temporal path rules guidance](https://www.sciencedirect.com/science/article/abs/pii/S0957417423003056). Expert Systems with Applications, 223, 119804. [Github](https://github.com/DMKE-Lab/TPRG) 10 | 11 | **The Journal of Supercomputing** 12 | 13 | [1] (TKGA) Wang, Z., You, X., & Lv, X. (2023). [A relation enhanced model for temporal knowledge graph alignment](https://link.springer.com/article/10.1007/s11227-023-05670-w). The Journal of Supercomputing, 1-23. 14 | 15 | **Information Systems** 16 | 17 | [1] (RITI) Liu, R., Yin, G., Liu, Z., & Tian, Y. (2023). [Reinforcement learning with time intervals for temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S030643792300128X). Information Systems, 102292. 18 | 19 | **Information Sciences** 20 | 21 | [1] (T-GAE) Hou, X., Ma, R., Yan, L., & Ma, Z. (2023). [T-GAE: A Timespan-Aware Graph Attention-based Embedding Model for Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0020025523008101). Information Sciences, 119225. 22 | 23 | [2] (TASTER) Wang, X., Lyu, S., Wang, X., Wu, X., & Chen, H. (2023). [Temporal knowledge graph embedding via sparse transfer matrix](https://www.sciencedirect.com/science/article/pii/S0020025522015122). Information Sciences, 623, 56-69. 24 | 25 | [3] (TLmod) Bai, L., Yu, W., Chai, D., Zhao, W., & Chen, M. (2023). [Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules](https://www.sciencedirect.com/science/article/pii/S0020025522013871). Information Sciences, 621, 22-35. 26 | 27 | **IEEE/ACM Transactions on Audio, Speech, and Language Processing** 28 | 29 | [1] (TARGAT) Xie, Z., Zhu, R., Liu, J., Zhou, G., & Huang, J. X. (2023). [TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding](https://ieeexplore.ieee.org/abstract/document/10141863/). IEEE/ACM Transactions on Audio, Speech, and Language Processing. 30 | 31 | **Applied Intelligence** 32 | 33 | [1] (TBDRI) Yu, M., Guo, J., Yu, J., Xu, T., Zhao, M., Liu, H., ... & Yu, R. (2023). [TBDRI: block decomposition based on relational interaction for temporal knowledge graph completion](https://link.springer.com/article/10.1007/s10489-022-03601-5). Applied Intelligence, 53(5), 5072-5084. 34 | 35 | [2] (GLANet) Wang, J., Lin, X., Huang, H., Ke, X., Wu, R., You, C., & Guo, K. (2023). [GLANet: temporal knowledge graph completion based on global and local information-aware network](https://link.springer.com/article/10.1007/s10489-023-04481-z). Applied Intelligence, 1-17. 36 | 37 | [3] (ChronoR-CP) Li, M., Sun, Z., Zhang, W., & Liu, W. (2023). [Leveraging semantic property for temporal knowledge graph completion](https://link.springer.com/article/10.1007/s10489-022-03981-8). Applied Intelligence, 53(8), 9247-9260. 38 | 39 | [4] (TIAR) Mu, C., Zhang, L., Ma, Y., & Tian, L. (2023). [Temporal knowledge subgraph inference based on time-aware relation representation](https://link.springer.com/article/10.1007/s10489-023-04833-9). Applied Intelligence, 53(20), 24237-24252. 40 | 41 | [5] (TNTSimplE) He, P., Zhou, G., Zhang, M., Wei, J., & Chen, J. (2023). [Improving temporal knowledge graph embedding using tensor factorization](https://link.springer.com/article/10.1007/s10489-021-03149-w). Applied Intelligence, 53(8), 8746-8760. 42 | 43 | **Neural Networks** 44 | 45 | [1] (TFSC) Zhang, H., & Bai, L. (2023). [Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism](https://www.sciencedirect.com/science/article/pii/S0893608023000552). Neural Networks, 161, 371-381. [Github](https://github.com/DMKE-Lab/TFSC) 46 | 47 | [2] Shao, P., Liu, T., Che, F., Zhang, D., & Tao, J. (2023). [Adaptive pseudo-Siamese policy network for temporal knowledge prediction](https://www.sciencedirect.com/science/article/pii/S0893608023000047). Neural Networks. 48 | 49 | [3] Bai, L., Han, S., & Zhu, L. (2024). [Multi-Hop Interpretable Meta Learning for Few-Shot Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0893608024009109). Neural Networks, 106981. 50 | 51 | **Neurocomputing** 52 | 53 | [1] Shao, P., He, J., Li, G., Zhang, D., & Tao, J. (2023). [Hierarchical Graph Attention Network for Temporal Knowledge Graph Reasoning](https://www.sciencedirect.com/science/article/pii/S0925231223005131). Neurocomputing, 126390. 54 | 55 | [2] (TANGO) Wang, Z., Ding, D., Ren, M., & Conti, M. (2023). [TANGO: A Temporal Spatial Dynamic Graph Model for Event Prediction](https://www.sciencedirect.com/science/article/pii/S0925231223003727). Neurocomputing, 126249. 56 | 57 | **IEEE Transactions on Neural Networks and Learning Systems** 58 | 59 | [1] (QDN) Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., & Yin, B. (2023). [QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10132432/). IEEE Transactions on Neural Networks and Learning Systems. [Github](https://github.com/jiapuwang/QDN-A-Quadruplet-Distributor-Network-for-Temporal-Knowledge-Graph-Completion) 60 | 61 | **Journal of Systems Science and Systems Engineering** 62 | 63 | [1] Yan, Z., & Tang, X. (2023). [Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph](https://link.springer.com/article/10.1007/s11518-023-5561-0). Journal of Systems Science and Systems Engineering, 32(2), 206-221. 64 | 65 | **Engineering Applications of Artificial Intelligence** 66 | 67 | [1] (RoAN) Bai, L., Ma, X., Meng, X., Ren, X., & Ke, Y. (2023). [RoAN: A relation-oriented attention network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S095219762300492X). Engineering Applications of Artificial Intelligence, 123, 106308. [Github](https://github.com/DMKE-Lab/RoAN) 68 | 69 | **Future Generation Computer Systems** 70 | 71 | [1] (TAL-TKGC) Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y., & Sun, Y. (2023). [Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0167739X23000195). Future Generation Computer Systems. 72 | 73 | **Cognitive Computation** 74 | 75 | [2] (MsCNN) Liu, W., Wang, P., Zhang, Z., & Liu, Q. (2023). [Multi-Scale Convolutional Neural Network for Temporal Knowledge Graph Completion](https://link.springer.com/article/10.1007/s12559-023-10134-7). Cognitive Computation, 1-7. 76 | 77 | **ACM Transactions on Knowledge Discovery from Data** 78 | 79 | [1] (DuCape) Zhang, S., Liang, X., Tang, H., Zheng, X., Zhang, A. X., & Ma, Y. [DuCape: Dual Quaternion and Capsule Network Based Temporal Knowledge Graph Embedding](https://dl.acm.org/doi/abs/10.1145/3589644). ACM Transactions on Knowledge Discovery from Data. 80 | 81 | **IEEE Transactions on Knowledge and Data Engineering** 82 | 83 | [1] Li, Y., Chen, H., Li, Y., Li, L., Philip, S. Y., & Xu, G. (2023). [Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation](https://arxiv.org/abs/2111.12262). IEEE Transactions on Knowledge and Data Engineering. [Github](https://github.com/Abigale001/TMER-RL) 84 | 85 | **Knowledge-Based Systems** 86 | 87 | [1] (RLAT) Bai, L., Chai, D., & Zhu, L. (2023). [RLAT: Multi-hop temporal knowledge graph reasoning based on Reinforcement Learning and Attention Mechanism](https://www.sciencedirect.com/science/article/pii/S0950705123002642). Knowledge-Based Systems, 269, 110514. 88 | 89 | [2] Luo, X., Zhu, A., Zhang, J., & Shao, J. (2024). HierarT: Multi-hop temporal knowledge graph forecasting with hierarchical reinforcement learning. Knowledge-Based Systems, 112164. 90 | 91 | **Journal of Computational Design and Engineering** 92 | 93 | [1] (MetaRT) Zhu, L., Xing, Y., Bai, L., & Chen, X. (2023). [Few-shot link prediction with meta-learning for temporal knowledge graphs](https://academic.oup.com/jcde/article-abstract/10/2/711/7069330). Journal of Computational Design and Engineering, 10(2), 711-721. 94 | 95 | **Entropy** 96 | 97 | [1] 🔥 (IMF) Du, Z., Qu, L., Liang, Z., Huang, K., Cui, L., & Gao, Z. (2023). [IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs](https://www.mdpi.com/1099-4300/25/4/666). Entropy, 25(4), 666. [Github](https://github.com/lfxx123/TKBC) 98 | 99 | **Complex & Intelligent Systems** 100 | 101 | [1] (FTMO) Zhu, L., Bai, L., Han, S., & Zhang, M. (2023). [Few-shot temporal knowledge graph completion based on meta-optimization](https://link.springer.com/article/10.1007/s40747-023-01146-9). Complex & Intelligent Systems, 9(6), 7461-7474. [Github](https://github.com/DMKE-Lab/FTMO) 102 | 103 | **World Wide Web** 104 | 105 | [1] (FTMF) Bai, L., Zhang, M., Zhang, H., & Zhang, H. (2023). [FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism](https://link.springer.com/article/10.1007/s11280-022-01091-6). World Wide Web, 26(3), 1243-1270. [Github](https://github.com/DMKE-Lab/FTMF) 106 | 107 | **DMKD** 108 | 109 | [1] (OSLT) Ma, R., Mei, B., Ma, Y., Zhang, H., Liu, M., & Zhao, L. (2023). [One-shot relational learning for extrapolation reasoning on temporal knowledge graphs](https://link.springer.com/article/10.1007/s10618-023-00935-7). Data Mining and Knowledge Discovery, 1-18. -------------------------------------------------------------------------------- /journal/2024.md: -------------------------------------------------------------------------------- 1 | ## 2024 2 | 3 | **Knowledge-Based Systems** 4 | 5 | [1] Yue, L., Ren, Y., Zeng, Y., Zhang, J., Zeng, K., Wan, J., & Zhou, M. (2024). [Complex expressional characterizations learning based on block decomposition for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0950705124002260). Knowledge-Based Systems, 111591. 6 | 7 | [2] Zhu, L., Zhang, H., & Bai, L. (2024). [Hierarchical pattern-based complex query of temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0950705123010493). Knowledge-Based Systems, 284, 111301. 8 | 9 | [3] Huang, H., Xie, L., Liu, M., Lin, J., & Shen, H. (2024). [An embedding model for temporal knowledge graphs with long and irregular intervals](https://www.sciencedirect.com/science/article/pii/S0950705124005276). Knowledge-Based Systems, 111893. 10 | 11 | [4] Guo, J., Yu, J., Zhao, M., Yu, M., Yu, R., Xu, L., ... & Li, X. (2024). [TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S095070512400902X). Knowledge-Based Systems, 112268. 12 | 13 | [5] Hu, J., Zhu, Y., Teng, F., & Li, T. (2024). [Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism](https://www.sciencedirect.com/science/article/pii/S0950705124009146). Knowledge-Based Systems, 112280. 14 | 15 | **Applied Intelligence** 16 | 17 | [1] Wang, J., Wu, R., Wu, Y., Zhang, F., Zhang, S., & Guo, K. (2024). [MPNet: temporal knowledge graph completion based on a multi-policy network](https://link.springer.com/article/10.1007/s10489-024-05320-5). Applied Intelligence, 1-17. [Github](https://github.com/Mike-RF/MPNet) 18 | 19 | [2] Ma, Q., Zhang, X., Ding, Z., Gao, C., Shang, W., Nong, Q., ... & Jin, Z. (2024). [Temporal knowledge graph reasoning based on evolutional representation and contrastive learning](https://link.springer.com/article/10.1007/s10489-024-05767-6). Applied Intelligence, 1-19. 20 | 21 | **ACM TKDD** 22 | 23 | [1] Li, X., Zhou, H., Yao, W., Li, W., Liu, B., & Lin, Y. (2024). [Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning](https://dl.acm.org/doi/abs/10.1145/3648366). ACM Transactions on Knowledge Discovery from Data. 24 | 25 | **Information Science** 26 | 27 | [1] (THOR) Lee, Y. C., Lee, J., Lee, D., & Kim, S. W. (2024). [Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion](https://www.sciencedirect.com/science/article/abs/pii/S0020025523014421?casa_token=clrOVF8uX6sAAAAA:CbR7aGo0qkRov9ss746qvbbzKHTyqhD9jLULqbGBFigjj8pOiRq7Vd2e9_xbnLO_sZlDjU-DmLo). Information Sciences, 654, 119857. 28 | 29 | > Extended version from: Y. -C. Lee, J. Lee, D. Lee and S. -W. Kim, ["THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks,"](https://ieeexplore.ieee.org/document/10027723) 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 1035-1040, doi: 10.1109/ICDM54844.2022.00127. [Github](https://github.com/EJHyun/THOR) 30 | 31 | [2] (Joint-MTComplEx) Zhang, F., Chen, H., Shi, Y., Cheng, J., & Lin, J. (2024). [Joint framework for tensor decomposition-based temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S002002552301438X). Information Sciences, 654, 119853. 32 | 33 | [3] (DGTL) Liu, Z., Li, Z., Li, W., & Duan, L. (2024). [Deep Graph Tensor Learning for Temporal Link Prediction](https://www.sciencedirect.com/science/article/abs/pii/S0020025523016717). Information Sciences, 120085. [Github](https://github.com/xiaolaosao/DGTL) 34 | 35 | [4] (CRmod) Zhu, L., Chai, D., & Bai, L. (2024). [CRmod: Context-Aware Rule-Guided reasoning over temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0020025524002561). Information Sciences, 120343. [Github](https://github.com/DMKE-Lab/CRmod) 36 | 37 | [5] Dai, Y., Guo, W., & Eickhoff, C. (2024). [Wasserstein adversarial learning based temporal knowledge graph embedding](https://www.sciencedirect.com/science/article/pii/S002002552301647X?via%3Dihub). Information Sciences, 659, 120061. 38 | 39 | [6] Xu, X., Jia, W., Yan, L., Lu, X., Wang, C., & Ma, Z. (2024). [Spatiotemporal knowledge graph completion via diachronic and transregional word embedding](https://www.sciencedirect.com/science/article/pii/S0020025524003906). Information Sciences, 120477. 40 | 41 | [7] Guo, J., Zhao, M., Yu, J., Yu, R., Song, J., Wang, Q., ... & Yu, M. (2024). [EHPR: Learning Evolutionary Hierarchy Perception Representation based on Quaternion for Temporal Knowledge Graph Completion](https://www.sciencedirect.com/science/article/pii/S0020025524013239). Information Sciences, 121409. 42 | 43 | [8] Si, Y., Hu, X., Cheng, Q., Liu, X., Liu, S., & Huang, J. (2025). [Coherence mode: Characterizing local graph structural information for temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0020025524012714). Information Sciences, 686, 121357. 44 | 45 | **Information Fusion** 46 | 47 | [1] (MvTuckER) Wang, H., Yang, J., Yang, L. T., Gao, Y., Ding, J., Zhou, X., & Liu, H. (2024). [MvTuckER: Multi-view knowledge graphs represention learning based on tensor tucker model](https://www.sciencedirect.com/science/article/abs/pii/S1566253524000277). Information Fusion, 102249. 48 | 49 | **Information Processing & Management** 50 | 51 | [1] (STKGR-PR) Meng, X., Bai, L., Hu, J., & Zhu, L. (2024). [Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping](https://www.sciencedirect.com/science/article/pii/S0306457323003424). Information Processing & Management, 61(2), 103605. [Github](https://github.com/DMKE-Lab/STKGR-PR) 52 | 53 | [2] Ma, J., Li, K., Zhang, F., Wang, Y., Luo, X., Li, C., & Qiao, Y. (2024). [TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion](https://www.sciencedirect.com/science/article/pii/S0306457324002073). Information Processing & Management, 61(6), 103848. 54 | 55 | [3] Bai, L., Zhang, H., An, X., & Zhu, L. (2025). [Few-shot multi-hop reasoning via reinforcement learning and path search strategy over temporal knowledge graphs](https://www.sciencedirect.com/science/article/pii/S0306457324003601). Information Processing & Management, 62(3), 104001. 56 | 57 | **Expert Systems with Applications** 58 | 59 | [1] (CDRGN-SDE) Zhang, D., Feng, W., Wu, Z., Li, G., & Ning, B. (2024). [CDRGN-SDE: Cross-Dimensional Recurrent Graph Network with neural Stochastic Differential Equation for temporal knowledge graph embedding](https://www.sciencedirect.com/science/article/pii/S095741742400160X). Expert Systems with Applications, 123295. [Github](https://github.com/zhangdddong/CDRGN-SDE) 60 | 61 | [2] (TPComplEx) Yang, J., Ying, X., Shi, Y., & Xing, B. (2024). [Tensor decompositions for temporal knowledge graph completion with time perspective](https://www.sciencedirect.com/science/article/pii/S0957417423017694). Expert Systems with Applications, 237, 121267. [Github](https://github.com/Jinfa/TPComplEx) 62 | 63 | **Frontiers of Computer Science** 64 | 65 | [1] (EvolveKG) Liu, J., Yu, Z., Guo, B., Deng, C., Fu, L., Wang, X., & Zhou, C. (2024). [EvolveKG: a general framework to learn evolving knowledge graphs](https://link.springer.com/article/10.1007/s11704-022-2467-9). Frontiers of Computer Science, 18(3), 183309. 66 | 67 | **Neural networks** 68 | 69 | [1] Shao, P., Tao, J., & Zhang, D. (2024). [Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction](https://www.sciencedirect.com/science/article/pii/S0893608024000704). Neural Networks, 106146. 70 | 71 | [2] Bai, L., Li, N., Li, G., Zhang, Z., & Zhu, L. (2024). [Embedding-based Entity Alignment of Cross-Lingual Temporal Knowledge Graphs](https://www.sciencedirect.com/science/article/pii/S0893608024000595). Neural Networks, 106143. 72 | 73 | [3] 🔥 Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., Han, J., & Pan, S. (2024). [An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph](https://www.sciencedirect.com/science/article/pii/S0893608024001436). Neural Networks, 106219. [Github](https://github.com/mxadorable/ILR-IR) 74 | 75 | [4] Zhang, J., Sun, M., Huang, Q., & Tian, L. (2024). [PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S0893608024004404). Neural Networks, 106516. [Github](https://github.com/KcAcoZhang/PLEASING) 76 | 77 | **Engineering Applications of Artificial Intelligence** 78 | 79 | [1] Zhu, L., Zhao, W., & Bai, L. (2024). [Quadruple mention text-enhanced temporal knowledge graph reasoning](https://www.sciencedirect.com/science/article/pii/S0952197624002161). Engineering Applications of Artificial Intelligence, 133, 108058. [Github](https://github.com/DMKE-Lab/QM-mod) 80 | 81 | **Journal of Intelligent Information Systems** 82 | 83 | [1] Du, C., Li, X., & Li, Z. (2024). [Semantic-enhanced reasoning question answering over temporal knowledge graphs](https://link.springer.com/article/10.1007/s10844-024-00840-5). Journal of Intelligent Information Systems, 1-23. 84 | 85 | **Artificial Intelligence** 86 | 87 | [1] Dong, H., Wang, P., Xiao, M., Ning, Z., Wang, P., & Zhou, Y. (2024). [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://www.sciencedirect.com/science/article/pii/S0004370224000213). Artificial Intelligence, 104085. [Github](https://github.com/hhdo/TiPNN) 88 | 89 | **IEEE Transactions on Fuzzy Systems** 90 | 91 | [1] Ji, H., Yan, L., & Ma, Z. (2023). [FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation](https://ieeexplore.ieee.org/document/10198282). IEEE Transactions on Fuzzy Systems. 92 | 93 | [2] An, X., Bai, L., Zhou, L., & Song, J. (2024). [Few-shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network](https://ieeexplore.ieee.org/abstract/document/10643313/). IEEE Transactions on Fuzzy Systems. 94 | 95 | [3] Wang, C., Yan, L., & Ma, Z. (2024). [Fuzzy Event Knowledge Graph Embedding Through Event Temporal and Causal Transfer](https://ieeexplore.ieee.org/abstract/document/10646584/). IEEE Transactions on Fuzzy Systems. 96 | 97 | **Electronics** 98 | 99 | [1] 🔥 Xu, H., Bao, J., Li, H., He, C., & Chen, F. (2024). [A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion](https://www.mdpi.com/2079-9292/13/4/742). Electronics, 13(4), 742. 100 | 101 | [2] Liu, Y., Shen, Y., & Dai, Y. (2024). [Enhancing Temporal Knowledge Graph Representation with Curriculum Learning](https://www.mdpi.com/2079-9292/13/17/3397). Electronics, 13(17), 3397. 102 | 103 | **Neurocomputing** 104 | 105 | [1] He, M., Zhu, L., & Bai, L. (2024). [ConvTKG: A query-aware convolutional neural network-based embedding model for temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S092523122400451X). Neurocomputing, 127680. 106 | 107 | [2] Song, J., Bai, L., An, X., & Zhou, L. (2024). [Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learning](https://www.sciencedirect.com/science/article/pii/S0925231224017909). Neurocomputing, 129019. 108 | 109 | [3] Zhu, Y., Ma, T., Sun, S., Rong, H., Bian, Y., & Huang, K. (2024). [RTA: A reinforcement learning-based temporal knowledge graph question answering model](https://www.sciencedirect.com/science/article/pii/S092523122401765X). Neurocomputing, 128994. 110 | 111 | **IEEE TKDE** 112 | 113 | [1] Zhang, F., Zhang, Z., Zhuang, F., Zhao, Y., Wang, D., & Zheng, H. (2024). [Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement](https://ieeexplore.ieee.org/abstract/document/10504973/). IEEE Transactions on Knowledge and Data Engineering. 114 | 115 | [2] Liao, L., Zheng, L., Shang, J., Li, X., & Chen, F. (2024). [ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge Graph](https://ieeexplore.ieee.org/abstract/document/10777929/). IEEE Transactions on Knowledge and Data Engineering. 116 | 117 | **Tsinghua Science and Technology** 118 | 119 | [1] Han, Y., Lu, G., Zhang, S., Zhang, L., Zou, C., & Wen, G. (2024). [A Temporal Knowledge Graph Embedding Model Based on Variable Translation](https://ieeexplore.ieee.org/abstract/document/10517975/). Tsinghua Science and Technology, 29(5), 1554-1565. 120 | 121 | **Applied Soft Computing** 122 | 123 | [1] Bai, L., Chen, M., & Xiao, Q. (2024). Multi-Hop Temporal Knowledge Graph Reasoning with Multi-Agent Reinforcement Learning. Applied Soft Computing, 111727. [Github](https://github.com/DMKE-Lab/MA-TPath) 124 | 125 | **IEEE Transactions on Cybernetics** 126 | 127 | [1] Wang, J., Wang, B., Gao, J., Pan, S., Liu, T., Yin, B., & Gao, W. (2024). [MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion](https://ieeexplore.ieee.org/abstract/document/10535899/). IEEE Transactions on Cybernetics. 128 | 129 | **IEEE Transaction on AI** 130 | 131 | [1] Yang, J., Huang, C., Yang, X., Yang, L. T., Gao, Y., & Liu, C. (2024). Temporal Knowledge Extrapolation Based on Fine-grained Tensor Graph Attention Network for Responsible AI. IEEE Transactions on Artificial Intelligence. 132 | 133 | **IAENG International Journal of Computer Science** 134 | 135 | [1] Huang, C., & Zhong, Y. (2024). A Novel Approach for Representing Temporal Knowledge Graphs. IAENG International Journal of Computer Science, 51(6). 136 | 137 | **Information Systems** 138 | 139 | [1] Jia, W., Ma, R., Niu, W., Yan, L., & Ma, Z. (2024). [SFTe: Temporal Knowledge Graphs Embedding for Future Interaction Prediction](https://www.sciencedirect.com/science/article/pii/S0306437924000814). Information Systems, 102423. 140 | 141 | **IEEE/ACM Transactions on Audio, Speech, and Language Processing** 142 | 143 | [1] Gao, Y., Qiao, L., Huang, Z., Kan, Z., He, Y., & Li, D. (2024). [Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph](https://ieeexplore.ieee.org/abstract/document/10771697/). IEEE/ACM Transactions on Audio, Speech, and Language Processing. -------------------------------------------------------------------------------- /journal/2025.md: -------------------------------------------------------------------------------- 1 | ## 2025 2 | 3 | **Applied Intelligence** 4 | 5 | Zhang, C., Li, W., Mo, Y., Tang, W., Li, H., & Zeng, Z. (2025). BHRAM: a knowledge graph embedding model based on bidirectional and heterogeneous relational attention mechanism. Applied Intelligence, 55(3), 245. 6 | 7 | **Neural Networks** 8 | 9 | [1] Bai, L., Han, S., & Zhu, L. (2025). [Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion](https://www.sciencedirect.com/science/article/pii/S0893608024009109). Neural Networks, 183, 106981. 10 | 11 | [2] Chen, T., Yang, L., Wang, Z., & Long, J. (2025). A rule-and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs. Neural Networks, 107186. 12 | 13 | **Science China Information Sciences** 14 | 15 | [1] Cai, W., Li, M., Shi, X., Fan, Y., Zhu, Q., & Jin, H. (2025). RE-SEGNN: recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting. Science China Information Sciences, 68(2), 122104. 16 | 17 | **Expert Systems With Applications** 18 | 19 | [1] Ma, R., Wang, L., Wu, H., Gao, B., Wang, X., & Zhao, L. (2025). Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning. Expert Systems With Applications, 260, 125366. 20 | 21 | **Information Processing & Management** 22 | 23 | [1] Bai, L., Zhang, H., An, X., & Zhu, L. (2025). Few-shot multi-hop reasoning via reinforcement learning and path search strategy over temporal knowledge graphs. Information Processing & Management, 62(3), 104001. 24 | 25 | [2] Li, Q., & Wu, G. (2025). Explainable reasoning over temporal knowledge graphs by pre-trained language model. Information Processing & Management, 62(1), 103903. 26 | 27 | [3] Xu, W., Liu, B., Peng, M., Jiang, Z., Jia, X., Liu, K., ... & Peng, M. (2025). Historical facts learning from Long-Short Terms with Language Model for Temporal Knowledge Graph Reasoning. Information Processing & Management, 62(3), 104047. 28 | 29 | **Pattern Recognition** 30 | 31 | [1] Zhang, J., Hui, B., Zhu, X., Tian, L., & Hua, F. (2025). Temporal knowledge graph reasoning based on discriminative neighboring semantic learning. Pattern Recognition, 111392. 32 | 33 | **IEEE Transactions on Neural Networks and Learning Systems** 34 | 35 | [1] Qian, Y., Wang, X., Sun, F., & Pan, L. (2025). Compressing Transfer: Mutual Learning-Empowered Knowledge Distillation for Temporal Knowledge Graph Reasoning. IEEE Transactions on Neural Networks and Learning Systems. 36 | 37 | **Information Fusion** 38 | 39 | [1] Yang, R., Zhu, J., Man, J., Liu, H., Fang, L., & Zhou, Y. (2025). GS-KGC: A generative subgraph-based framework for knowledge graph completion with large language models. Information Fusion, 117, 102868. 40 | 41 | **Knowledge-Based Systems** 42 | 43 | [1] Zhu, J., Hu, J., Bai, D., Fu, Y., Zhou, J., & Chen, D. (2025). Multi-dimension rotations based on quaternion system for modeling various patterns in temporal knowledge graphs. Knowledge-Based Systems, 113114. 44 | 45 | **PeerJ Computer Science** 46 | 47 | [1] Liu, W., Hasikin, K., Khairuddin, A. S. M., Liu, M., & Zhao, X. (2025). A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning. PeerJ Computer Science, 11, e2595. 48 | 49 | **Data & Knowledge Engineering** 50 | 51 | [1] Zhu, L., Duan, X., & Bai, L. (2025). SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph. Data & Knowledge Engineering, 155, 102372. 52 | 53 | **Symmetry** 54 | 55 | [1] Chen, Y., Li, X., Liu, Y., & Hu, T. (2025). Integrating Transformer Architecture and Householder Transformations for Enhanced Temporal Knowledge Graph Embedding in DuaTHP. Symmetry, 17(2), 173. 56 | 57 | **Expert Systems** 58 | 59 | [1] Chen, H., Zhang, M., & Chen, Z. (2025). Temporal Knowledge Graph Reasoning Based on Dynamic Fusion Representation Learning. Expert Systems, 42(2), e13758. 60 | 61 | **Information Sciences** 62 | 63 | [1] Guo, J., Zhao, M., Yu, J., Yu, R., Song, J., Wang, Q., ... & Yu, M. (2025). EHPR: Learning evolutionary hierarchy perception representation based on quaternion for temporal knowledge graph completion. Information Sciences, 688, 121409. 64 | 65 | [2] Si, Y., Hu, X., Cheng, Q., Liu, X., Liu, S., & Huang, J. (2025). Coherence mode: Characterizing local graph structural information for temporal knowledge graph. Information Sciences, 686, 121357. 66 | 67 | **Engineering Applications of Artificial Intelligence** 68 | 69 | [1] Nguyen, N. T., Ngo, T., Hoang, N., & Le, T. (2025). FTPComplEx: A flexible time perspective approach to temporal knowledge graph completion. Engineering Applications of Artificial Intelligence, 139, 109717. 70 | 71 | **Journal of Intelligent Information Systems** 72 | 73 | [1] Chen, Z., & Wu, J. (2025). Temporal knowledge graph completion based on product space and contrastive learning of commonsense. Journal of Intelligent Information Systems, 1-20. 74 | 75 | **Computer Science and Information Systems** 76 | 77 | [1] Li, S., Wang, Q., Li, Z., & Zhang, L. (2025). TPBoxE: Temporal knowledge graph completion based on time probability box embedding. Computer Science and Information Systems, (00), 6-6. --------------------------------------------------------------------------------