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.

翻译：本体知识库在结构化领域特定知识方面发挥着至关重要的作用，是构建高效知识管理系统的基础。然而，其传统的手工开发方式在可扩展性、一致性和适应性方面面临重大挑战。生成式人工智能的最新进展，特别是大型语言模型，为自动化和增强本体知识库的开发提供了前景广阔的解决方案。本文提出了一种结构化的迭代方法，利用LLMs优化知识获取、自动化本体工件生成并实现持续精炼循环。我们通过一个专注于车辆销售领域用户上下文配置文件本体开发的详细案例研究来展示该方法。主要贡献包括显著加速本体构建过程、提升本体一致性、有效缓解偏见以及增强本体工程过程的透明度。我们的研究结果凸显了将LLMs集成到本体开发中的变革潜力，特别是在提升知识管理系统的可扩展性、集成能力和整体效率方面。