## 4 Experiments
### Discussion
#### 4.3.1 Quantitative Evaluation Metrics
However, fine-tuning results in complete failure across all iterations. The first fine-tuned model identifies no true positives, leading to a precision and recall of 0%. Subsequent iterations show no recovery, with precision and recall remaining at 0%. Even though the final iteration reports a precision of 80%, this figure is deceptive, as the absence of any true positives means the model fails to provide any meaningful object property identification. The disparity in performance between the two models can be attributed to several factors. First, GPT-4's larger parameter space and advanced architecture equipped it to handle the logical reasoning and complexity required for OE tasks. Conversely, Mistral 7B's smaller size limited its ability to generalize and represent nuanced relationships effectively. Second, the fine-tuning methodologies differed significantly. GPT-4 employed an iterative approach, building upon previous fine-tuned models, which facilitated cumulative learning and enhanced performance. Mistral 7B, due to platform constraints, required re-uploading datasets for each iteration, disrupting continuity and hindering knowledge retention. Third, while GPT-4 consistently understood and executed the role-playing prompts designed to guide the ontology generation process, Mistral 7B struggled with prompt comprehension and produced incomplete or redundant outputs. Despite GPT-4's superior performance, both models fell short of fully replicating the benchmark ontology. This highlights a critical limitation in their ability to generalize domain-specific knowledge solely from foundational texts. For instance, GPT-4 and Mistral 7B struggled with object properties, with the former achieving a maximum precision of 66.6% but low recall, while the latter failed to produce any meaningful outputs in later iterations. These shortcomings suggest a gap in the models' ability to integrate complex, domain-specific relationships and semantic structures. To address these challenges, further experimentation is essential. Incorporating real-world domain-specific datasets, such as those derived from Search and Rescue (SAR) missions, could significantly enhance the models' contextual understanding and enable them to generate more robust and realistic ontologies. Such datasets would provide a richer foundation for fine-tuning, allowing the models to align more closely with practical requirements. Additionally, expanding the scope and depth of competency questions could help capture intricate relationships and guide the models toward improved precision and recall. This would not only refine their outputs but also make them more relevant to real-world applications. In conclusion, while GPT-4 demonstrated greater potential for OE tasks compared to Mistral 7B, both models exhibited limitations that underscore the need for more comprehensive datasets and refined methodologies. Incorporating domain-specific data and leveraging iterative, feedback-driven approaches would likely yield more robust ontologies and bridge the gap between automated generation and expert-crafted benchmarks. These advancements could establish large language models as indispensable tools for domain-specific knowledge representation, ultimately enhancing their utility in OE and related fields.