Augmenting Knowledge Graph Hierarchies Using Neural Transformers
<|reference_start|>Augmenting Knowledge Graph Hierarchies Using Neural Transformers: 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.<|reference_end|>
@article{sharma2024augmenting,
    title={Augmenting Knowledge Graph Hierarchies Using Neural Transformers},
    author={Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, Tracy King},
    journal={arXiv preprint arXiv:2404.08020},
    year={2024},
    doi={10.1007/978-3-031-56069-9_35},
    archivePrefix={arXiv},
    eprint={2404.08020},
    primaryClass={cs.AI cs.CL cs.DL cs.IR cs.LG}
}