# Ontology engineering with Large Language Models

**Authors:** Patricia Mateiu, Adrian Groza  
**arXiv:** [2307.16699](https://arxiv.org/abs/2307.16699)  
**Date:** 2023-07-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.16699v1/

## Key points

- Authors Patricia Mateiu and Adrian Groza focus on enriching ontologies through automated translation using Description Logic.
- They utilize a fine-tuned GPT-3 model to convert natural language sentences into OWL Functional Syntax.
- The resulting axioms are crucial for enhancing ontologies in a human-supervised manner.
- The approach demonstrates the effectiveness of combining advanced language models with domain-specific knowledge for improved results.
- The publicly available Protge plugin is highlighted as a valuable resource for researchers and practitioners in this field.
- The paper showcases the potential of artificial intelligence and knowledge representation, emphasizing promising advancements in the field.

## Summary

In their paper titled "Ontology engineering with Large Language Models," authors Patricia Mateiu and Adrian Groza delve into the task of enriching ontologies through automated translation using Description Logic. By leveraging a fine-tuned GPT-3 model, they successfully convert natural language sentences into OWL Functional Syntax. The resulting axioms play a crucial role in enhancing ontologies in a human-supervised manner. This innovative approach showcases the potential of combining advanced language models with domain-specific knowledge to achieve more efficient and accurate results. The publicly available Protge plugin offers a valuable resource for researchers and practitioners in this field. Overall, this paper serves as a testament to the intersection of artificial intelligence and knowledge representation, highlighting promising advancements in this area.