## 4 Experiments
### Discussion  
#### 4.3.1 Quantitative Evaluation Metrics  
The comparison between GPT-4 and Mistral 7B for generating domain-specific ontologies through fine-tuning reveals significant contrasts in their capabilities and highlights areas requiring further refinement. Both models were fine-tuned using foundational OE texts, and their performance was evaluated based on precision, recall, and F1 scores compared to a benchmark ontology crafted by human experts. The results from Section 4.2 illustrate the differing abilities of the two models to integrate and represent complex ontological knowledge. GPT-4 demonstrated a notable capacity for incorporating foundational knowledge and iteratively improving its outputs through successive fine-tuning. Although the initial results were modest, each fine-tuning iteration led to incremental improvements in precision, such as reaching 83% in the second iteration, indicating better accuracy in identifying relevant classes. However, its recall metrics, such as 12.5% in the same iteration, reflect persistent challenges in capturing the breadth of the benchmark ontology. More specifically, for class identification, the precision of the basic model starts at 69% and improves to 83% by the second fine-tuned model, an increase of approximately 20.3%. In the final iteration, precision stabilizes at 81.25%, confirming a consistent upward trend. However, recall progresses more slowly, increasing from 11.25% in the basic model to 16.25% in the third fine-tuned model, a relative improvement of 44.4%. This discrepancy between precision and recall indicates that while GPT-4 becomes increasingly accurate, it still struggles to identify a broader set of relevant classes, reflected in its F1 score, which improves from 0.1935 in the basic model to 0.2708 in the final iteration--an overall improvement of approximately 39.9%. In the evaluation of object properties, GPT-4 begins with no true positives in its basic form, yielding a precision and recall of 0%. However, fine-tuning progressively improves precision, which reaches 66.6% by the third iteration. Despite this, recall remains extremely low, improving only marginally from 0% to 3.33%. As a result, the F1 score, which starts at 0, achieves only 0.0634 by the final iteration. GPT-4 consistently displayed a capacity to adapt and refine its understanding of the domain, effectively leveraging the prompts and incorporating iterative feedback. In contrast, Mistral 7B faced more pronounced limitations. While the first fine-tuning iteration demonstrated the model's ability to generate classes, the presence of duplicate classes and a relatively low precision of 14.2% highlighted significant issues with internal consistency. Subsequent iterations showed slight improvements in precision and recall but remained far behind GPT-4 in both metrics. By the third iteration, Mistral 7B completely failed to produce meaningful outputs, with precision and recall dropping to 0%, indicating a complete breakdown in its ability to align with the benchmark ontology. These results underscore the inherent architectural and computational constraints of Mistral 7B compared to GPT-4. More specifically, for class identification, the basic model achieves a precision of 68.4% and a recall of 15.25%, resulting in an F1 score of 0.2626. However, the first fine-tuned iteration shows a sharp decline in performance, with precision dropping to 14.2%, a decrease of 79.2%, while recall plummets to 3.75%, reflecting a 75% reduction. Although precision recovers to 50% in the second fine-tuning iteration, recall remains low at 7.5%, and the final iteration fails entirely, producing no true positives and reducing recall to 0%. Despite an improvement in precision to 80% in the third iteration, this result is misleading, as it arises in the absence of any identified classes, effectively nullifying practical utility. In the case of object properties, Mistral 7B exhibits similar trends. The basic model begins with a precision of 23.5% and a recall of 6.66%, yielding an F1 score of 0.1038.