# Ontology engineering with Large Language Models

arXiv: https://arxiv.org/abs/2307.16699 · HTML: https://mlsanity.com/paper/2307.16699/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)

## Built on by (most cited first, 17 of 17)

- [OLaLa: Ontology Matching with Large Language Models](https://mlsanity.com/paper/2311.03837/) (2023)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [LLM-empowered knowledge graph construction: A survey](https://mlsanity.com/paper/2510.20345/) (2025)
- [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024)
- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [Do LLMs Really Adapt to Domains? An Ontology Learning Perspective](https://mlsanity.com/paper/2407.19998/) (2024)
- [Retrieval-Augmented Generation of Ontologies from Relational Databases](https://mlsanity.com/paper/2506.01232/) (2025)
- [Ontology Learning with LLMs: A Benchmark Study on Axiom Identification](https://mlsanity.com/paper/2512.05594/) (2025)
- [Towards Ontology-Enhanced Representation Learning for Large Language Models](https://mlsanity.com/paper/2405.20527/) (2024)
- [Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2405.14012/) (2024)
- [OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs](https://mlsanity.com/paper/2607.17963/) (2026)
- [Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applications](https://mlsanity.com/paper/2502.11108/) (2025)
- [Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learning](https://mlsanity.com/paper/2309.17252/) (2023)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)
- [The Mercurial Top-Level Ontology of Large Language Models](https://mlsanity.com/paper/2405.01581/) (2024)
- [Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting](https://mlsanity.com/paper/2512.14288/) (2025)
- [JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications](https://mlsanity.com/paper/2606.28070/) (2026)

## Abstract (arXiv)

We tackle the task of enriching ontologies by automatically translating natural language sentences into Description Logic. Since Large Language Models (LLMs) are the best tools for translations, we fine-tuned a GPT-3 model to convert Natural Language sentences into OWL Functional Syntax. We employ objective and concise examples to fine-tune the model regarding: instances, class subsumption, domain and range of relations, object properties relationships, disjoint classes, complements, cardinality restrictions. The resulted axioms are used to enrich an ontology, in a human supervised manner. The developed tool is publicly provided as a Protge plugin.