Enhancing Knowledge Graph Construction Using Large Language Models

TL;DR

This paper explores how large language models like ChatGPT can enhance the automatic construction of knowledge graphs and ontologies from unstructured text, improving accuracy and relevance.

Contribution

It introduces a pipeline integrating LLMs with semantic technologies for knowledge graph and ontology creation, demonstrating improved accuracy over existing methods.

Findings

LLMs improve knowledge graph accuracy from raw text

Automatic ontology creation with LLMs yields more relevant graphs

Experiments confirm the effectiveness of the proposed approach

Abstract

The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for reasoning and inference is still a challenging task. This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. To evaluate this approach, we conducted several experiments using sustainability-related text as our use case. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text. Furthermore, we explored the potential of automatic ontology creation using foundation LLM models, which resulted in even more relevant and accurate knowledge graphs.