Toward a Method to Generate Capability Ontologies from Natural Language Descriptions
This addresses the problem of reducing manual effort in ontology modeling for developers and domain experts, though it is incremental as it builds on existing LLM techniques.
The paper tackles the manual and error-prone task of modeling capability ontologies by introducing an automated method using Large Language Models (LLMs) that requires only a natural language description, greatly reducing manual effort and streamlining the generation process.
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.