Summary of Croppable Knowledge Graph Embedding, by Yushan Zhu et al.
Croppable Knowledge Graph Embedding
by Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers propose a novel Knowledge Graph Embedding (KGE) training framework called MED, which enables the creation of a single KGE model that can be cropped to meet specific dimensional requirements for various scenarios. This approach eliminates the need to train new models from scratch, significantly improving efficiency and flexibility. The authors introduce a mutual learning mechanism, an evolutionary improvement mechanism, and a dynamic loss weight to balance losses adaptively. Experimental results demonstrate the effectiveness of MED on three KGE models across four standard datasets and three real-world application scenarios. Additionally, the paper shows that MED can be extended to language models like BERT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KGE is used in artificial intelligence for knowledge graphs. The problem is that when you need a new dimension, you have to train a whole new model from scratch. This takes a lot of time and makes it hard to use KGE for different tasks. To solve this, the researchers created a new way to train KGE models called MED. With MED, you can train one model and then “crop” it to get smaller versions that are perfect for specific tasks. The authors also developed some special techniques to make sure these smaller models work well and learn quickly. They tested MED on several datasets and scenarios and showed that it really works! |
Keywords
» Artificial intelligence » Bert » Embedding » Knowledge graph
