Development of Ontological Knowledge Bases by Leveraging Large Language Models

TL;DR

This paper presents a structured, iterative approach using Large Language Models to automate and improve the development of Ontological Knowledge Bases, addressing scalability and consistency challenges.

Contribution

It introduces a novel methodology leveraging LLMs for automating ontology artifact creation and continuous refinement, demonstrated through a vehicle sales domain case study.

Findings

Accelerated ontology construction process

Enhanced ontological consistency and transparency

Improved scalability and integration capabilities

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.