update.

.gitignore

@@ -0,0 +1,2 @@
+*.ipynb
+__pycache__

conferences/2014.md

@@ -1,7 +1,5 @@
 # 2014
 
-## Conference
-
 **EMNLP**
 
 [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

@@ -1,7 +1,5 @@
 # 2016
 
-## Conference
-
 **EMNLP**
 
 [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).

conferences/2017.md

@@ -1,7 +1,5 @@
 # 2017
 
-## Conference
-
 **ICML**
 
 [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.

conferences/2018.md

@@ -1,11 +1,9 @@
 # 2018
 
-## Conference
-
 **EMNLP**
 
-[3] 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).
+[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).
 
 [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)
 
-[1] (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.
+[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

@@ -1,9 +1,15 @@
 # 2019
 
-## Conference
-
 **ICLR**
 
 [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)
 
-[2] 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.
+[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.
+
+**ICTAI**
+
+[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.
+
+**WISE**
+
+[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

@@ -1,14 +1,12 @@
 # 2020 
 
-## Conferences
-
 **EMNLP**
 
 [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.
 
 > 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)
 
-[2] 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.
+[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.
 
 > 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)
 
@@ -24,6 +22,8 @@
 
 [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)
 
+[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).
+
 **IJCAI**
 
 [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)
@@ -44,6 +44,10 @@
 
 [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)
 
-## ArXiv
+**ICTAI**
+
+[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.
+
+**ArXiv**
 
-[14] 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.
+[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

@@ -1,7 +1,5 @@
 # 2021
 
-## Conferences
-
 **KDD**
 
 [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)
@@ -64,6 +62,18 @@
 
 [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)
 
-**NeurIPS 2022 Temporal Graph Learning Workshop**
+**ICFEICT**
+
+[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).
+
+**CKC**
+
+[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).
+
+**IJCNN**
+
+[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.
+
+**TGL Workshop**
 
 [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

@@ -1,12 +1,10 @@
 # 2022
 
-## Conferences ❄️ ❄️ ❄️
-
 **ICDM**
 
-[1] 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.
+[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.
 
-[2] 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.
+[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.
 
 **CIKM**
 
@@ -47,6 +45,8 @@
 [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.
 > 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)
 
+[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)
+
 **COLING**
 
 [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.
@@ -71,22 +71,22 @@
 
 [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. 
 
-**Automated Knowledge Base Construction 2022 Conference**
+**CAKBC**
 
 [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)
 
-**Joint European Conference on Machine Learning and Knowledge Discovery in Databases**
+**ECMLKDD**
 
 [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.
 
-**International Conference on Mobility, Sensing and Networking**
+**ICMSN**
 
 [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.
 
-**Conference on Empirical Methods in Natural Language Processing**
+**PRICAI**
 
-[24] 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)
+[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.
 
-## ArXiv
+**ICMLNLP** 
 
-[1] 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.
+[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).