Summary of Negative Sampling in Knowledge Graph Representation Learning: a Review, by Tiroshan Madushanka et al.
Negative Sampling in Knowledge Graph Representation Learning: A Review
by Tiroshan Madushanka, Ryutaro Ichise
First submitted to arxiv on: 29 Feb 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 comprehensive survey paper reviews various negative sampling (NS) methods that support Knowledge Graph Representation Learning (KGRL), a crucial component of AI applications such as knowledge construction and information retrieval. The authors categorize existing NS methods into six distinct categories, highlighting their advantages and disadvantages. Additionally, the paper identifies open research questions that serve as potential directions for future investigations. By generalizing fundamental NS concepts, this survey provides valuable insights for designing effective NS methods in the context of KGRL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Negative sampling is crucial for training Knowledge Graph Embedding (KGE) models, which encode entities and relations into lower-dimensional vectors. The quality of negative samples impacts the model’s accuracy. This paper reviews various NS methods that support KGRL, a key component of AI applications like link prediction and recommendation systems. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph » Representation learning
