Augmenting Knowledge Graph Hierarchies Using Neural Transformers

Sanat Sharma; Mayank Poddar; Jayant Kumar; Kosta Blank; Tracy King

doi:10.1007/978-3-031-56069-9_35

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arXiv · 2404.08020

Augmenting Knowledge Graph Hierarchies Using Neural Transformers

Sanat Sharma·Mayank Poddar·Jayant Kumar·Kosta Blank·Tracy King

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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.

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Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, Tracy King. 2024-04-11. Augmenting Knowledge Graph Hierarchies Using Neural Transformers. https://doi.org/10.1007/978-3-031-56069-9_35

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