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.
@article{arxiv.2404.08020,
title = {Augmenting Knowledge Graph Hierarchies Using Neural Transformers},
author = {Sanat Sharma and Mayank Poddar and Jayant Kumar and Kosta Blank and Tracy King},
journal= {arXiv preprint arXiv:2404.08020},
year = {2024}
}