## 4 Experiments
### Extended Experimentation  
To enhance the robustness of the models and their ability to generate domain-specific ontologies, we expanded our experimentation by integrating two additional, domain-specific, datasets. These datasets were designed with the expertise of the authoring team, under their capacity as domain experts, and aimed to address the unique requirements of SAR operations during wildfire incidents. The need for domain-specific datasets beyond basic OE knowledge emerged from the findings of previous experiments, where the results fell short of representing the full capabilities of the large language models (LLMs) and their fine-tuning potential. While the models demonstrated some ability to process foundational OE concepts, their outputs lacked the contextual richness and applicability needed for complex, real-world scenarios. This highlighted the importance of integrating targeted, domain-specific datasets to boost the models' performance and enable them to generate more robust and nuanced ontologies. The first dataset builds on the original set of 18 competency questions derived from the reference ontology. Recognizing gaps in coverage and the need for more granular representation of wildfire SAR operations, we subdivided the competency questions into subsections reflective of specific operational contexts as followed: Weather-Related Questions, Incident-Related Questions, Data from Human and Earth Observations, Missions and Services, Population and Community Impact, Environmental Impact and Rehabilitation, Technology and Modeling, Safety and Protocols and some Additional Questions. This structured expansion was guided by critical areas of knowledge that must be represented for effective SAR decision-making. By integrating domain expertise, we extended the dataset to 48 competency questions, ensuring comprehensive coverage of SAR ontology requirements. These competency questions were meticulously answered based on our domain expertise, forming a knowledge-rich dataset. The finalized Q&A pairs were uploaded into GPT-4 for JSONL conversion, ensuring consistency and accessibility for fine-tuning purposes. This expanded dataset aimed to provide nuanced insights and a broader perspective on SAR operations, enriching the model's training data and enabling it to represent complex, domain-specific scenarios accurately. The second dataset was constructed from incident report templates used by the Greek Fire Department during actual wildfire incidents. These templates encapsulate critical information recorded during operations, such as resource allocation, mission timelines, environmental conditions, and outcomes. These real-world data were translated into a textual format and subsequently processed through GPT-4 to generate Q&A pairs in JSONL format. This dataset brings an operationally grounded perspective to the model, incorporating the terminology, procedural details, and contextual nuances encountered in SAR missions. By aligning the training data with practical applications, the dataset serves to bridge the gap between theoretical ontology principles and real-world use cases. To evaluate the impact of these datasets, we fine-tuned the models in the configurations that each of them requires: on GPT-4 we followed the Separate Integration. Each dataset (expanded competency questions and incident reports) was uploaded independently to the last trained model (3 rd fine-tuned) for fine-tuning. On Mistral 7B we comply with Sequential Integration. Following the sequence used in previous experiments, we fine-tuned the models first with the expanded competency questions dataset, followed by the incident report dataset. This method simulated a cumulative knowledge-building process, where the foundational ontology knowledge was augmented with real-world operational data. Initial experiments suggest that incorporating these datasets leads to a noticeable improvement in the models' ability to generate robust, contextually accurate ontologies.