Toward a Method to Generate Capability Ontologies from Natural Language Descriptions

TL;DR

This paper introduces a novel method leveraging Large Language Models to automate the creation of capability ontologies from natural language descriptions, significantly reducing manual effort and improving accuracy.

Contribution

The paper presents an innovative LLM-based approach for automatic capability ontology modeling using few-shot prompting and iterative verification, streamlining the process.

Findings

Reduces manual effort in ontology creation

Automates verification of ontology correctness

Achieves high accuracy with minimal human intervention

Abstract

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.