This research introduces a Retrieval-Augmented Generation (RAG) approach that leverages Large Language Models to automate the traditionally manual process of creating competency questions for ontology engineering.

• Reduces the time-consuming, labor-intensive work previously required from domain experts
• Implements a specialized RAG architecture to enhance LLM performance in ontology development
• Provides a scalable solution for knowledge representation challenges
• Demonstrates how AI can support technical documentation and knowledge engineering workflows

This innovation matters for Engineering because it streamlines ontology development processes, potentially accelerating knowledge management system implementation across technical domains while maintaining quality and consistency.

A RAG Approach for Generating Competency Questions in Ontology Engineering