Knowledge graphs (KGs) are increasingly utilized for data integration, representation, and visualization. While KG population is critical, it is often costly, especially when data must be extracted from unstructured text in natural language, which presents challenges, such as ambiguity and complex interpretations. Large Language Models (LLMs) offer promising capabilities for such tasks, excelling in natural language understanding and content generation. However, their tendency to ``hallucinate'' can produce inaccurate outputs. Despite these limitations, LLMs offer rapid and scalable processing of natural language data, and with prompt engineering and fine-tuning, they can approximate human-level performance in extracting and structuring data for KGs. This study investigates LLM effectiveness for the KG population, focusing on the Enslaved.org Hub Ontology. In this paper, we report that compared to the ground truth, LLM's can extract ~90% of triples, when provided a modular ontology as guidance in the prompts.
@article{arxiv.2411.01612,
title = {Ontology Population using LLMs},
author = {Sanaz Saki Norouzi and Adrita Barua and Antrea Christou and Nikita Gautam and Andrew Eells and Pascal Hitzler and Cogan Shimizu},
journal= {arXiv preprint arXiv:2411.01612},
year = {2024}
}