## 5 Discussion  
The results from Tables 5 and 6 underscore the transformative impact of incorporating domain-specific datasets into the fine-tuning process of large language models (LLMs), particularly in comparison to the earlier experiments outlined in Tables 1-4. These domain-specific datasets, focusing on wildfire SAR operations, allowed both GPT-4 and Mistral 7B to achieve significantly better precision, recall, and F1 scores, demonstrating their enhanced ability to represent domain-specific knowledge comprehensively. In the earlier experiments (Table 1), GPT-4 exhibited limited recall and F1 scores even after iterative fine-tuning on basic OE knowledge. The baseline GPT-4 model achieved a precision of 69% but suffered from a recall as low as 11.25% and an F1 score of 0.1935. Similarly, Mistral 7B struggled with basic datasets, often failing to generate distinct classes without duplication, with minimal improvements across iterations. With the introduction of domain-specific datasets, both models demonstrated significant performance gains in class generation. As shown in Table 5:  
* GPT-4 improved its recall to 40%, doubling its performance compared to prior experiments. The addition of SAR-specific knowledge enriched the model's understanding, reflected in its ability to generate 72 distinct classes while maintaining a precision of 44.4%. * Mistral 7B, previously outperformed by GPT-4 in class generation, surpassed GPT-4 in this iteration. It achieved a recall of 48.75% and an F1 score of 0.5379, emphasizing its ability to better integrate domain-specific insights into a semantically rich ontology with 65 classes. This represents a marked improvement over its earlier iterations (Table 3), where precision and recall were as low as 14.2% and 3.75%, respectively. Object property generation results also highlight the value of domain-specific datasets (Table 6). Previously (Table 2), both GPT-4 and Mistral 7B struggled significantly with object property identification, with Mistral 7B failing to generate any true positives in later iterations and GPT-4 showing limited recall improvements. * GPT-4, in its fine-tuned version with SAR datasets, generated 32 object properties, achieving a precision of 43.75% and an F1 score of 0.3043. Although this was a significant improvement over its earlier attempts (maximum recall of 3.33% in Table 2), it still lagged behind Mistral 7B in representing relational knowledge effectively. * Mistral 7B, benefiting from sequential fine-tuning with domain-specific datasets, excelled in this category. It generated 51 object properties, achieving a recall of 45% and an F1 score of 0.4864. This improvement reflects the model's ability to map complex relationships, such as those between incidents, resources, and environmental factors, a crucial aspect of wildfire SAR operations. The comparative analysis across these experiments underscores the critical role of domain-specific datasets in OE. While basic OE knowledge provides a foundational understanding, it falls short of enabling LLMs to address real-world, complex domains effectively. The SAR-specific datasets bridged this gap, enriching the models' knowledge bases with operationally grounded concepts and relationships. The inclusion of expanded competency questions and incident report templates allowed the models to achieve the following:  
* Better address the complexity of domain knowledge, including subclass hierarchies and property relationships. * Improve recall, particularly in identifying classes and properties relevant to wildfire SAR operations. * Enhance F1 scores, indicating a balanced ability to reduce false positives while capturing more true positives. These results suggest that fine-tuning LLMs with targeted, domain-specific data are pivotal for achieving robust knowledge representation. Domain-specific datasets not only enable the models to better emulate human expert ontologies but also highlight their ability to capture operational nuances, such as environmental impacts, mission timelines, and resource allocation.