Summary of Selective Temporal Knowledge Graph Reasoning, by Zhongni Hou et al.
Selective Temporal Knowledge Graph Reasoning
by Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Jiafeng Guo, Xueqi Cheng
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors propose an abstention mechanism for Temporal Knowledge Graph (TKG) reasoning to prevent uncertain predictions in real-world applications. Existing TKG reasoning models make indiscriminate predictions, which can be risky. The abstention mechanism, Confidence Estimator with History (CEHis), estimates confidence and allows the model to selectively predict or abstain based on low confidence. CEHis considers both the certainty of current predictions and the accuracy of historical predictions. The authors demonstrate the effectiveness of CEHis on two benchmark datasets using representative TKG reasoning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make smart decisions in the future by looking at what has happened before. Right now, machines are really good at making predictions based on past events, but sometimes they’re not very confident in their answers and that can lead to mistakes. The authors of this paper want to fix this problem by creating a special tool called CEHis (Confidence Estimator with History). This tool helps the machine figure out how sure it is about its prediction and then decide whether or not to make it, based on how accurate it has been in the past. |
Keywords
* Artificial intelligence * Knowledge graph
