## 4 Experiments  
### Overview  
The overarching goal of our work is to develop a fine-tuned model that can generate and curate structured semantic knowledge from carefully selected foundational texts. In this context, the experiments presented in this section aim to (i) substantiate the validity and effectiveness of the proposed approach, (ii) quantify the extent to which ontologies produced by the proposed method are accurate and complete and (iii) provide insight for the adjustment of hyperparameters and the fine-tuning cycles. In the initial stage of our experimental framework, our primary aim was to tailor a state-of-the-art large language model (LLM)--specifically GPT-4--to meet the specialized demands of OE. By drawing upon authoritative works and core research in the domain, we sought to guide the model's internal representations toward not only understanding complex ontological concepts but also systematically distilling them into organized, readily accessible knowledge components. Central to this effort was the translation of rich, often unstructured textual sources into a more structured question-answer (Q&A) format aligned with OE requirements. This reformatting step was intended to bring clarity and consistency to the knowledge extraction process, ensuring that the resultant information could be seamlessly integrated into ontology design and maintenance workflows. As stated above, the key focus was to produce model outputs that are both faithful to the source material and directly applicable to engineering tasks. In doing so, we established the foundation for subsequent experiments: refining the model's capabilities, assessing its performance against domain criteria, and ultimately advancing the state of the art in ontology-based systems development. To ensure the model's training material was both authoritative and comprehensive, we began by selecting a curated set of foundational texts spanning the core principles, practices, and methodological frameworks of OE. These texts, specifically \"Semantic Web for the Working Ontologist\" by [PERSON] and [PERSON] [19], \"A Semantic Web Primer\" by [PERSON], [PERSON], [PERSON], and [PERSON] [20], and \"An Introduction to OE\" by [PERSON] [21], were chosen for their well-established reputation and broad coverage of the field. Drawing upon these works provided a rich, multifaceted source of knowledge that encompasses everything from basic terminology and conceptual underpinnings to advanced reasoning techniques and modeling strategies. Incorporating content from these references allowed us to capture the full spectrum of OE concerns--from high-level conceptual design to the practicalities of semantic data integration. By anchoring our data preparation workflow in these authoritative texts, we created a knowledge base that is both robust and aligned with established industry and research standards, ensuring that the fine-tuning process would build upon a solid intellectual foundation. A crucial step in our data preparation process involved segmenting the source material into more granular, manageable units. Given that each of our selected foundational texts was structured into chapters typically spanning 20 to 30 pages, we chose the chapter as a natural starting point for segmentation. This segmentation was motivated both by practical considerations--such as the limits on the amount of text that could be processed effectively by the model at once--and by the desire to maintain thematic coherence within each unit of analysis. We aimed to ensure that each chapter's content was rich enough to provide meaningful context for knowledge extraction, yet not so large as to overwhelm the model's processing capacity. To further refine this process, we adopted a working assumption that each page, on average, could yield approximately three to four Q&A pairs. Aggregated at the chapter level, this translated to roughly 60 Q&A pairs per chapter, providing a balanced datasetthat captured a chapter's conceptual breadth without diluting its core insights.