  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.
