Development of Ontological Knowledge Bases by Leveraging Large Language Models
This work addresses scalability and efficiency issues in knowledge management systems for domain-specific applications, representing an incremental improvement by applying existing LLM technology to ontology engineering.
The paper tackles the challenges of manual development of Ontological Knowledge Bases (OKBs) by introducing a structured, iterative methodology that leverages Large Language Models (LLMs) to automate and enhance the process, resulting in significantly accelerated construction, improved consistency, and effective bias mitigation in a vehicle sales domain case study.
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.