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.

翻译：知识图谱是组织、推荐和排序数据的有用工具。知识图谱中的层次结构在提升数据理解与分类方面具有显著优势。本研究利用大型语言模型在现有知识图谱中生成并增强层次结构。对于小型（节点数<10万）领域特定知识图谱，我们发现少样本提示与单次生成相结合的方法效果良好，而大型知识图谱可能需要循环生成。我们提出了增强层次结构的技术，使得知识图谱中意图的覆盖范围提升了98%，颜色的覆盖范围提升了99%。