On the Use of Large Language Models to Generate Capability Ontologies

TL;DR

This paper explores how Large Language Models can assist in automatically generating capability ontologies from natural language, demonstrating promising results with minimal errors even for complex capabilities.

Contribution

It presents a novel study on using LLMs with various prompting techniques to generate and evaluate capability ontologies, highlighting their potential in automating ontology creation.

Findings

Generated ontologies are nearly error-free for complex capabilities.

Different prompting techniques impact the quality of generated ontologies.

Semi-automated quality assessment effectively evaluates ontology correctness.

Abstract

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.