Summary of A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings, by Jeffrey Sardina et al.
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
by Jeffrey Sardina, John D. Kelleher, Declan O’Sullivan
First submitted to arxiv on: 13 Dec 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 paper provides a comprehensive survey on the relationships between Knowledge Graph Embedding Models (KGEMs) and graph structure. KGEMs are widely used to solve the link prediction task in various settings. Despite their effectiveness, there is still a lack of understanding on how KGEMs react differently to KG structure, which can be a source of bias and impact performance. The paper aims to address this gap by exploring established relationships between KGEMs and graph structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper looks at how machine learning models work with large networks of knowledge (knowledge graphs) and how they are affected by the way these networks are structured. This is important because it can help reduce errors and biases in the predictions made by these models. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph » Machine learning
