A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
| Approach: | They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data. |
| Outcome: | The proposed model outperforms SOTA methods on the link prediction task. |
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| Challenge: | Existing methods for lifelong TKG reasoning only address part of the challenges. |
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| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
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| Challenge: | Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes. |
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian
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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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