# On the Use of Large Language Models to Generate Capability Ontologies

arXiv: https://arxiv.org/abs/2404.17524 · HTML: https://mlsanity.com/paper/2404.17524/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Enhancing Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2305.04676/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing](https://mlsanity.com/paper/2209.09632/) (2022)
- [Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0](https://mlsanity.com/paper/2403.17209/) (2024)

## Built on by (most cited first, 5 of 5)

- [Toward a Method to Generate Capability Ontologies from Natural Language Descriptions](https://mlsanity.com/paper/2406.07962/) (2024)
- [xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models](https://mlsanity.com/paper/2507.08432/) (2025)
- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information](https://mlsanity.com/paper/2506.10613/) (2025)
- [From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation](https://mlsanity.com/paper/2606.02167/) (2026)

## Abstract (arXiv)

Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology experts. However, Large Language Models (LLMs) have shown that they can generate machine-interpretable models from natural language text input and thus support engineers / ontology experts. Therefore, this paper investigates how LLMs can be used to create capability ontologies. We present a study with a series of experiments in which capabilities with varying complexities are generated using different prompting techniques and with different LLMs. Errors in the generated ontologies are recorded and compared. To analyze the quality of the generated ontologies, a semi-automated approach based on RDF syntax checking, OWL reasoning, and SHACL constraints is used. The results of this study are very promising because even for complex capabilities, the generated ontologies are almost free of errors.