To achieve a flexible and adaptable system, capability ontologies are
increasingly leveraged to describe functions in a machine-interpretable way.
However, modeling such complex ontological descriptions is still a manual and
error-prone task that requires a significant amount of effort and ontology
expertise. This contribution presents an innovative method to automate
capability ontology modeling using Large Language Models (LLMs), which have
proven to be well suited for such tasks. Our approach requires only a natural
language description of a capability, which is then automatically inserted into
a predefined prompt using a few-shot prompting technique. After prompting an
LLM, the resulting capability ontology is automatically verified through
various steps in a loop with the LLM to check the overall correctness of the
capability ontology. First, a syntax check is performed, then a check for
contradictions, and finally a check for hallucinations and missing ontology
elements. Our method greatly reduces manual effort, as only the initial natural
language description and a final human review and possible correction are
necessary, thereby streamlining the capability ontology generation process.

通过使用大型语言模型（LLMs），本研究提出了一种创新的方法来自动化能力本体建模，该方法通过少样本提示技术仅需自然语言描述即可自动插入预定义的提示，从而大大减少了手动工作量并简化了能力本体生成过程。

从自然语言描述生成能力本体论方法探索

Toward a Method to Generate Capability Ontologies from Natural Language  Descriptions

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.

使用大型语言模型（LLMs）生成能力本体论，通过一系列试验以及不同提示技术和不同 LLMs 生成的能力来分析生成的本体论的质量