Ontology Population using LLMs

TL;DR

This paper explores the use of Large Language Models for populating knowledge graphs from unstructured text, demonstrating they can extract approximately 90% of triples with proper guidance, despite challenges like hallucination.

Contribution

It presents a novel approach using LLMs with prompt engineering to effectively populate KGs, specifically applied to the Enslaved.org Hub Ontology.

Findings

LLMs can extract about 90% of triples compared to ground truth.

Prompt guidance significantly improves extraction accuracy.

LLMs offer a scalable solution for KG population from unstructured text.

Abstract

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.