# Augmenting Knowledge Graph Hierarchies Using Neural Transformers

arXiv: https://arxiv.org/abs/2404.08020 · HTML: https://mlsanity.com/paper/2404.08020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction](https://mlsanity.com/paper/2307.01128/) (2023)

## Abstract (arXiv)

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.