🤖 AI Summary
Traditional ontology construction relies heavily on manual effort, suffering from poor scalability, low consistency, and limited adaptability. This work proposes a structured, iterative approach leveraging large language models (LLMs) to automate knowledge extraction and ontology component generation by integrating domain-specific context, while enabling continuous refinement. The method substantially accelerates the ontology development process, enhances semantic consistency, mitigates model bias, and improves transparency in the engineering workflow. Evaluation through a case study on constructing a user persona ontology in the automotive sales domain demonstrates that the proposed approach efficiently yields a highly consistent, scalable, and domain-specific knowledge base.
📝 Abstract
Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems. However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.