## 4 Experiments
### Extended Experimentation
#### 4.4.1 Gpt4  
The new fine-tuned GPT-4 model demonstrated exceptional performance in generating a robust SAR ontology. By incorporating domain-specific datasets such as the expanded competency questions and wildfire incident reports, GPT-4 effectively utilized its role-playing capabilities to structure and generate a cohesive ontology. The model generated an ontology that aligns closely with practical SAR requirements, including detailed classifications of classes, object properties, and data properties (Tables 5 and 6). As seen in the ontology metrics, GPT-4 produced a structure with 72 distinct classes, 32 object properties, and 7 data properties, effectively capturing both theoretical and operational aspects. The model also achieved logical coherence, as evidenced by the 284 logical axioms and the robust use of subclass relationships. These results highlight GPT-4's ability to translate domain-specific knowledge into comprehensive and structured ontologies, showcasing its potential as a reliable tool for OE in specialized fields like wildfire SAR operations. More specifically, for class identification, the GPT-4 basic model achieved a precision of 69%, a recall of 11.25%, and an F1 score of 0.1935. In comparison, the new fine-tuned GPT-4 model demonstrated a precision of 44.4%, showing a 35.6% decrease, but its recall \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of Obj.** & **True** & **False** & **False** & \multirow{2}{*}{**Precision**} & **Recall** & **F1** \\  & **Properties** & **Positive** & **Positives** & **Negatives** & & & **Score** \\ \hline Reference & 60 & & & & & & \\ \hline GPT-4 Basic Model & 5 & 0 & 5 & 60 & 0\% & 0\% & 0 \\ \hline Mistral 7B Basic Model & 17 & 4 & 13 & 56 & 23.5\% & 6.66\% & 0.1038 \\ \hline New Fine-Tuned GPT-4 & 32 & 14 & 18 & 46 & 43.75\% & 23.33\% & 0.3043 \\ \hline New Fine-Tuned Mistral 7B & 51 & 27 & 24 & 33 & 52.94\% & 45\% & 0.4864 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of object property generation metrics between the fine-tuned GPT-4 and Mistral 7B models after incorporating domain-specific datasets and their basic models, emphasizing significant improvements in recall and F1 scores over previous experiments relying solely on OE training data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of Classes** & **True** & **False** & **False** & \multirow{2}{*}{**Precision**} & **Recall** & **F1 Score** \\  & **Classes** & **Positive** & **Positives** & & & & \\ \hline Reference & 80 & & & & & & \\ \hline GPT-4 Basic Model & 13 & 9 & 4 & 71 & 69\% & 11.25\% & 0.1935 \\ \hline Mistral 7B Basic Model & 19 & 14 & 6 & 67 & 68.4\% & 15.25\% & 0.2626 \\ \hline New Fine-Tuned GPT-4 & 72 & 32 & 40 & 48 & 44.4\% & 40\% & 0.4210 \\ \hline New Fine-Tuned Mistral 7B & 65 & 39 & 26 & 41 & 60\% & 48.75\% & 0.5379 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of class generation metrics between the fine-tuned GPT-4 and Mistral 7B models after incorporating domain-specific datasets and their basic models, highlighting significant improvements in recall and F1 scores compared to previous experiments limited to OE training data. improved dramatically to 40%, marking a 255.5% improvement. This significant recall increase contributed to the F1 score rising to 0.4210, an overall improvement of 117.5%. For object property identification, the GPT-4 basic model started with 0% precision and 0% recall, producing an F1 score of 0. After fine-tuning, the new GPT-4 model achieved a precision of 43.75% and a recall of 23.33%, representing significant improvements over the baseline. The F1 score increased to 0.3043, highlighting the model's enhanced ability to identify object properties and maintain a reasonable balance between precision and recall. In summary, the new fine-tuned GPT-4 model demonstrates substantial gains over the basic model, particularly in recall, which shows remarkable improvement across both tasks.