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**NIPS**

[1] 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/
[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/

**IJCAI**

[1] 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)
[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)

**AAAI**

[1] 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)
[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)

[2] 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)
[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)

[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.

> 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)
**ICLR**

[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.

**ECCS**

[2] 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).
[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.

**DASFAA**

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**PAKDD**

[1] 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)
[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)

**IJCNN**

[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.

[2] 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.
[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.

**ICASSP**

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**SIGIR**

[1] 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
[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
> 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.
[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).
[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).

[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).
[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).

**WWW**

[1] 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

[2] 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

[3] (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)
[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)

[4] 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
[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

**CIKM**

[1] 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/abs/10.1145/3583780.3615231). In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4059-4063).

[2] Yan, H., Ge, Y., Wang, H., Zhang, D., & Yang, Y. (2023, October). [Logistics Audience Expansion via Temporal Knowledge Graph](https://dl.acm.org/doi/abs/10.1145/3583780.3614695). In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4879-4886).
[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).

**ICMM**

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**ACL-EMNLP**

[1] 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.
[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.

[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.

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**ICWS**

[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.
[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.

**CCAI**

[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.

**ICDE**

[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.

**IWCEOA**

[1] 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.
[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.

**TGL Workshop**

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