Abstract
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.
Abstract (translated)
知识图谱是有用的一些工具来组织、推荐和排序数据。知识图谱中的层次结构在提高数据在知识图谱中的理解和划分方面具有显著的优势。这项工作利用了大型语言模型生成和增强现有知识图谱中的层次结构。对于小（<100,000个节点）领域特定的KG，我们发现少数shot提示与一次生成相结合效果很好，而较大的KG可能需要循环生成。我们提出了增强层次结构的技术，在知识图中使意图和颜色的覆盖率分别增加了98%和99%。
URL
https://arxiv.org/abs/2404.08020
