Summary of Augmenting Knowledge Graph Hierarchies Using Neural Transformers, by Sanat Sharma et al.
Augmenting Knowledge Graph Hierarchies Using Neural Transformers
by Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, Tracy King
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR); 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 leverages large language models to generate and augment hierarchies in existing knowledge graphs. By combining few-shot prompting with one-shot generation, the authors show that this approach works well for small domain-specific knowledge graphs (less than 100,000 nodes). For larger knowledge graphs, cyclical generation may be required. The paper presents techniques for augmenting hierarchies, which leads to significant improvements in coverage, increasing by 98% for intents and 99% for colors in the tested knowledge graph. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called language models to make an existing database of information better organized. It finds that this method works well for small groups of data, but might need to be used multiple times for larger groups. The researchers also show how to make the organization even better, which helps a lot – it found 98% more relevant information about certain topics and 99% more relevant information about colors in their test database. |
Keywords
» Artificial intelligence » Few shot » Knowledge graph » One shot » Prompting
