Papers with inaccurate estimation
Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
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| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |