update.

README.md

@@ -127,6 +127,9 @@ Some papers on Temporal Knowledge Graph Embedding and Reasoning
 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)
    _Yu, C., Luo, T., Wang, J., Cao, Z._
 
+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)
+   _Cheng, K., Ahmed, N. K., Rossi, R. A., Willke, T., & Sun, Y. (2024)._
+
 ### 2023
 
 1. **A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects**, ArXiv, 2023. [paper](https://arxiv.org/abs/2308.02457)
@@ -198,6 +201,8 @@ Some papers on Temporal Knowledge Graph Embedding and Reasoning
 
 [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)
 
+[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.
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 **ACM TKDD**
 
 [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.
@@ -218,6 +223,10 @@ Some papers on Temporal Knowledge Graph Embedding and Reasoning
 
 [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.
 
+[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.
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+[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.
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 **Information Fusion**
 
 [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.
@@ -264,10 +273,16 @@ Some papers on Temporal Knowledge Graph Embedding and Reasoning
 
 [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.
 
+[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.
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+[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.  
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 **Electronics**
 
 [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.
 
+[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.
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 **Neurocomputing**
 
 [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.

conferences/2024.md

@@ -6,6 +6,8 @@
 
 [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.
 
+[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.
+
 **ICLR**
 
 [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.
@@ -155,6 +157,8 @@
 
 [1] Jia, N., & Yao, C. (2024). ShallowBKGC: a BERT-enhanced shallow neural network model for knowledge graph completion. PeerJ Computer Science, 10, e2058.
 
+[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.
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 ## ArXiv
 
 [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)
@@ -192,3 +196,11 @@
 [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.
 
 [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.
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+[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.
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+[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.
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+[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.
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+[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.