## 4 Experiments
### Results
#### 4.2.1. GPT-4 Fine-Tuning
The same automated hyperparameters were retained: three epochs, a batch size of 1, an LR multiplier of 2, and a temperature of 1. This iteration completed in just 56 min, reflecting a relatively quick adaptation to the new knowledge. When we re-ran the same OE prompt, the model once again assumed the prescribed role-playing format, producing an iterative conversation among the Knowledge Engineer, Domain Expert, and Knowledge Worker. This consistency in role adherence suggests that the foundational instructions from the first iteration had effectively \"set the stage\", allowing the model to integrate new conceptual material without losing the interactive structure. Crucially, the results (Tables 1 and 2) indicate a marked improvement over the first iteration. Compared to the initial fine-tuned model, the second iteration revealed both an increase in true positives and a better balance between precision and recall. While the first iteration's fine-tuned model demonstrated high precision but struggled to identify a broad range of relevant concepts (resulting in low recall), the second iteration improved recall, capturing more of the classes and properties present in the reference ontology. As a consequence, the F1 score--an indicator of the model's overall effectiveness--rose substantially. In practical terms, these enhancements mean that by supplementing the training data with content from a Semantic Web Primer, the model gained exposure to a broader spectrum of OE principles and examples. This enrichment allowed it to recognize and align with more elements of the expert-crafted reference ontology, thereby elevating its performance from merely being conservative and selective (high precision, low coverage) to a model that can identify a more representative set of relevant concepts (improved recall) while retaining strong precision. In short, the second fine-tuning iteration not only consolidated the model's grasp of established ontology concepts but also expanded its reach, making it more adept at reflecting the richness and diversity of a benchmark ontology constructed by human experts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Method**} & **Number of Obj.** & **True** & **False** & **False** & \multirow{2}{*}{**Precision**} & **Recall** & **F1 Score** \\  & **Properties** & **Positive** & **Positives** & & & & \\ \hline Reference Ontology & 60 & & & & & & \\ \hline GPT-4 Basic Model & 5 & 0 & 5 & 60 & 0\% & 0\% & 0 \\ \hline GPT-4 Fine-Tuned Model 1 & 10 & 3 & 7 & 57 & 30\% & 5\% & 0.0857 \\ \hline GPT-4 Fine-Tuned Model 2 & 4 & 1 & 3 & 59 & 25\% & 1.66\% & 0.0312 \\ \hline GPT-4 Fine-Tuned Model 3 & 3 & 2 & 1 & 58 & 66.6\% & 3.33\% & 0.0634 \\ \hline \end{tabular}
\end{table} 
Table 2: A summary of the GPT-4 model’s performance on identifying and classifying object properties at various stages of fine-tuning, measured against a human expert reference ontology. The table provides true positives, false positives, false negatives, and the resulting precision, recall, and F1 scores for each iteration. In the third fine-tuning iteration, we integrated an additional dataset derived from \"An introduction to ontology engineering\" by [PERSON] [21], supplementing the model's existing training from the two previous sources. Using the same automated hyperparameters (3 epochs, batch size of 1, LR multiplier of 2, and a temperature of 1), this fine-tuning process took approximately 52 min. Although the prompt remained structured around a three-role iterative discussion scenario, the model's response this time favored a more direct approach to ontology construction. It still recognized the roles, but was less dialog-oriented and more focused on systematically adding classes and properties to meet the competency requirements. When compared to the previous two fine-tuning stages, the third iteration shows a notable evolution in the model's performance metrics (Tables 1 and 2).