Summary of Context-aware Adapter Tuning For Few-shot Relation Learning in Knowledge Graphs, by Ran Liu et al.
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
by Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach for few-shot relation learning in knowledge graphs (KGs) is proposed, addressing limitations in existing meta-learning techniques. The method, called RelAdapter, enhances the adaptation process by incorporating contextual information about target relations and adapting meta-knowledge in a parameter-efficient manner. Experimental results on three benchmark KGs demonstrate the superiority of RelAdapter over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RelAdapter is a new way to help computers learn from small amounts of information about relationships between things in knowledge graphs. Right now, computers have trouble learning when there’s not enough information. But with RelAdapter, computers can adapt and learn more effectively by using the right information from each relationship. This makes it better at predicting missing relationships. |
Keywords
* Artificial intelligence * Few shot * Meta learning * Parameter efficient
