Augmenting Knowledge Graph Hierarchies Using Neural Transformers
Sanat Sharma Mayank Poddar Jayant Kumar Kosta Blank Tracy King
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.