Augmenting KG Hierarchies Using Neural Transformers
摘要
This work leverages neural transformers to generate hierarchies in an existing knowledge graph. For small ( \({<}\) 10,000 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. Hierarchy coverage increased by 98% for intents and 95% for colors.