Summary of Fast and Continual Knowledge Graph Embedding Via Incremental Lora, by Jiajun Liu et al.
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
by Jiajun Liu, Wenjun Ke, Peng Wang, Jiahao Wang, Jinhua Gao, Ziyu Shang, Guozheng Li, Zijie Xu, Ke Ji, Yining Li
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper introduces a novel approach to continual knowledge graph embedding (CKGE), which efficiently learns new knowledge while preserving old knowledge. The proposed framework, called , incorporates an incremental low-rank adapter mechanism to alleviate catastrophic forgetting of old knowledge and accelerate fine-tuning for emerging new knowledge. The model isolates new knowledge in specific layers based on the influence between old and new knowledge graphs and employs adaptive rank allocation to adjust its rank scale adaptively. Experimental results show that can reduce training time by 34%-49% while achieving competitive link prediction performance on four public datasets, with an average MRR score of 21.0%. Furthermore, on two newly constructed datasets, the model saves 51%-68% training time and improves link prediction performance by 1.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from big databases that keep growing. Right now, most methods try to remember old information but forget how to learn new things quickly. The researchers propose a new approach called , which can learn new information fast while still remembering the old stuff. They use special techniques to separate new and old knowledge and make it more efficient. Tests show that this method is faster and just as good as other methods on big datasets. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Knowledge graph
