  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.
