## 6 Conclusions  
This research paper explores the fine-tuning of LLMs, specifically GPT-4 and Mistral 7B, for OE tasks. Through comprehensive experiments, the impact of general OE training and domain-specific fine-tuning on the models' ability to generate robust and practical ontologies was evaluated. The results validated the identified research questions and revealed critical insights into the role of targeted data in enhancing the models' performance. The first research question (RQ1) related to the fine-tuning of LLMs on general OE concepts and the improvement in their performance was partially validated. While fine-tuning improved the baseline capabilities of both GPT-4 and Mistral 7B, the results highlighted a few limitations. GPT-4 demonstrated incremental improvements, achieving a precision of 81.25% and a modest recall increase from 11.25% to 16.25% in OWL class generation after three iterations. Mistral 7B, however, struggled with basic OE datasets, showing limited progress and regression in key metrics, with a recall of only 15.25% in preliminary experiments. These findings suggest that while foundational OE training is essential, it alone cannot shape models for the nuanced requirements of domain-specific knowledge representation. The second research question (RQ2)--that incorporating domain-specific datasets would enhance the practical utility of generated ontologies--was strongly validated. The addition of SAR-specific datasets, including expanded competency questions and real-world wildfire incident reports, significantly improved the models' performance. GPT-4's recall for class generation increased to 40%, with an F1 score of 0.4210, while its recall for object properties rose to 23.33%. More notably, Mistral 7B, which had previously lagged, surpassed GPT-4 in both class and object property metrics. It achieved a recall of 48.75% and an F1 score of 0.5379 for classes, and a recall of 45% and an F1 score of 0.4864 for object properties. These results underscore the transformative impact of domain-specific data in enabling models to accurately represent complex and operationally grounded knowledge structures. The key findings from this study highlight the necessity of combining domain-specific datasets and fine-tuning methodologies for maximizing LLM performance in specialized tasks. The integration of SAR-specific data enriched the models' understanding of key relationships and hierarchies, enabling the generation of semantically rigorous and practically relevant ontologies. Mistral 7B's performance in these experiments was particularly noteworthy, as it not only overcame its earlier struggles but also outperformed GPT-4 in recall and F1 scores for both classes and object properties. This demonstrates that smaller, cost-efficient models can achieve competitive results when trained on targeted data. This paper also revealed the importance of aligning training data with real-world applications. Earlier experiments using general OE data showed limited recall and low F1 scores, indicating a lack of contextual understanding. In contrast, domain-specific fine-tuning improved both models' ability to generate ontologies that aligned with practical needs, including detailed representations of SAR concepts such as weather impacts, resource allocation, and incident timelines. These improvements emphasize that domain-specific datasets bridge the gap between theoretical ontology principles and operational utility. In conclusion, this research confirms the critical role of fine-tuning with domain-specific datasets in enhancing the utilization of LLMs for OE. While general OE training plays the groundwork, it is insufficient for generating robust, application-ready ontologies. Domain-specific datasets enable models to achieve significantly higher recall and F1 scores, reflecting better contextual understanding and practical relevance.