Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting (2026.acl-long)
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| Challenge: | Temporal knowledge graphs (TKGs) require predicting future facts by modeling structural dependencies within each snapshot and temporal evolution across snapshots. |
| Approach: | They propose an encoder-agnostic framework that provides persistent entity states . EST maintains a global state buffer and aligns structural evidence with sequential signals . |
| Outcome: | Experiments show that EST improves diverse backbones and achieves state-of-the-art performance. |
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| Challenge: | Existing methods for temporal knowledge graph forecasting are insufficient structural contexts to learn effective representations. |
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| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
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| Challenge: | Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts. |
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STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)
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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan
| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
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