Summary of Sharing Parameter by Conjugation For Knowledge Graph Embeddings in Complex Space, By Xincan Feng et al.
Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
by Xincan Feng, Zhi Qu, Yuchang Cheng, Taro Watanabe, Nobuhiro Yugami
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper proposes a novel approach to Knowledge Graph Embedding (KGE) that addresses memory and training time consumption issues. The authors develop a parameter-sharing method, leveraging conjugate parameters for complex numbers in KGE models. This technique improves memory efficiency by 2x while maintaining comparable performance to state-of-the-art non-conjugate models, with faster or similar training times. The proposed method is demonstrated on two best-performing KGE models (5^{} and ) across five benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make computers learn from big collections of information about the world, called Knowledge Graphs. It’s like organizing a huge library where everything is connected. The problem is that this process takes up too much computer power and time. To fix this, the researchers came up with a clever trick that uses special math tricks to share information between different parts of the learning system. This makes it faster and more efficient without losing any accuracy. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
