## 2 Related Work
### Summary  
Leveraging LLMs for OE has demonstrated potential in automating and enhancing the OE process, yet it is underscored by critical limitations that suggest further research. Issues such as high computational demands and potential biases in training data, often derived from extensive web-crawled datasets, introduce data contamination and ethical concerns [1,2]. These issues highlight the essential need for continuous advancements in model architectures and training methodologies to elevate the reliability and fairness of LLM outputs. In the realm of OE, the integration of LLMs aims to streamline processes and decrease human labor, yet the importance of sustained human oversight to counteract biases and ensure the precision of generated ontologies is crucial [3, 4, 5]. Furthermore, the exploration of LLMs' automation capabilities in complex tasks like generating OWL outputs and translating natural language into Description Logic points out the challenges in achieving structurally and syntactically correct outputs [7,8]. Additionally, the research underscores the significant potential of LLMs in automating ontology construction for complex and dynamic scenarios like SAR operations, advocating collaborative methodologies that combine human expertise with LLMs' processing capabilities [9]. The need for domain-specific training and the integration of factual knowledge to mitigate the risk of generating incorrect responses is also emphasized [10]. Innovative frameworks that adapt LLMs to non-linguistic data further highlight the adaptability and broad applicability of these models [11]. Studies also point out the importance of tailored vocabularies and compliance with security regulations to enhance the effectiveness of LLMs in specific sectors like finance [12]. The benefits of fine-tuning LLMs for specialized medical tasks illustrate the models' utility in enhancing practical healthcare applications [13]. The strategic fine-tuning of LLMs using proprietary enterprise data showcases the necessity of domain-specific adaptations to maintain data privacy and performance [14]. Lastly, the need for a comprehensive guide on fine-tuning LLMs suggests the complexity of optimizing these models for various applications, advocating structured methodologies that address data preparation, model initialization, and performance enhancement [15].  
This study aims to address these identified limitations, such as poor precision and recall and limited handling of domain-specific concepts, by implementing a comprehensive fine-tuning and evaluation strategy that enhances the capabilities of LLMs for OE while tackling the practical challenges highlighted in the related studies. Through the integration of advanced prompting techniques and iterative refinement processes, this methodology substantially improves the LLMs' understanding and generation of domain-specific ontologies, ensuring higher structural and syntactical accuracy. Rigorous human validation is also incorporated at critical stages of model training and output generation to mitigate biases and ensure the practical utility of the generated content. Furthermore, this approach leverages the computational efficiencies of LLMs but with a structured oversight mechanism that involves domain experts in the loop, ensuring that the ontologies produced are not only technically sound but also practically applicable. This framework contributes to the broader application of LLMs, establishing a more robust, adaptable, and scalable methodology for automating knowledge-intensive processes.