On the Use of Large Language Models to Generate Capability Ontologies
This addresses the challenge of automating ontology creation for engineers and ontology experts, though it appears incremental as it applies existing LLM methods to a new domain.
The paper tackles the problem of creating complex capability ontologies, which typically require ontology experts, by using Large Language Models (LLMs) to generate them from natural language input. The results show that even for complex capabilities, the generated ontologies are almost free of errors.
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.