Збережено в:
| Автори: | , , , , |
|---|---|
| Формат: | Preprint |
| Опубліковано: |
2024
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| Предмети: | |
| Онлайн доступ: | https://arxiv.org/abs/2404.08020 |
| Теги: |
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Зміст:
- Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclical generation. We present techniques for augmenting hierarchies, which led to coverage increase by 98% for intents and 99% for colors in our knowledge graph.