## 4 Experiments
### Discussion
#### 4.3.2 Qualitative Analysis  
Beyond numerical evaluation metrics, it is essential to analyze the ontologies' structural integrity, semantic coherence, and practical usability. The qualitative assessment provides deeper insight into the generated ontologies' logical consistency, hierarchy formation, and alignment with expected domain-specific knowledge.  
1. Structural soundness: one of the key factors in assessing ontology quality is the correct hierarchical arrangement of concepts. GPT-4 consistently maintained structured and logically aligned hierarchies, preserving correct superclass-subclass (subsumption) relationships in most cases. However, it occasionally overgeneralized categories, grouping semantically distinct concepts under broader umbrella terms, requiring manual refinement. In contrast, Mistral 7B, particularly in early iterations, exhibited misclassifications and redundancy issues, such as assigning multiple conflicting parent classes to a single entity. However, with additional domain-specific fine-tuning, Mistral 7B showed significant improvement in class hierarchy consistency, correcting misplaced classifications and better differentiating subclass relationships.
2. Semantic coherence: the accuracy of relationships and constraints between entities is another important aspect of ontology quality. Mistral 7B struggled in initial fine-tuning iterations with incorrect domain and range assignments for object properties, often misaligning relationships (e.g., classifying an entity to an incorrect category). For example, it initially misclassified \"EmergencyResponder\" as both a subclass of \"MedicalPersonnel\" and \"Firefighter\", leading to inconsistencies. After incorporating domain-specific datasets, the model demonstrated improved understanding, correctly placing \"EmergencyResponder\" as a broader category encompassing both roles without conflicting subclass assignments. GPT-4, on the other hand, rarely misclassified relationships but occasionally omitted necessary inverse relations, requiring manual correction.
3. Usability and practical implementation: a key consideration in evaluating ontology quality is its real-world applicability. GPT-4-generated ontologies were more structured and required minimal post-processing, making them suitable for immediate integration into knowledge-based systems. However, they sometimes lacked specificity, necessitating additional manual refinement. Mistral 7B required more adjustments in earlier iterations, but after targeted fine-tuning, its outputs became increasingly reliable, showcasing improved adaptability to domain-specific needs. This suggests that while GPT-4 provides a strong initial structure, Mistral 7B has the potential to generate highly customized ontologies when trained with more targeted data.  
The evolution of Mistral 7B's ontology quality through iterative fine-tuning highlights the impact of dataset quality, competency question expansion, and domain adaptation. These refinements allowed the model to generate ontologies that better align with expert-defined structures. The comparison between GPT-4 and Mistral 7B demonstrates that while pre-trained models can produce well-formed structures, domain-specific fine-tuning plays a crucial role in refining their logical consistency and contextual accuracy.