| In Search and Rescue (SAR) missions, the integration of multiple sources of information may enhance operational efficiency and increase responsiveness significantly, improving situation awareness and aiding decision-making to save lives and mitigate incident impact. Ontologies are crucial for integrating and reasoning with data from diverse sources. Engineering a domain ontology for SAR can be better supported from an agile, collaborative, and iterative ontology engineering methodology (OEM), incorporating the interests of several stakeholders. Large Language Models (LLMs) can play a significant role in completing OEM processes. The goal of this work is to identify how ontology engineering (OE) tasks can be completed with the collaboration of LLMs and humans. The objectives of this paper are, a) to present preliminary exploration of LLMs to generate domain ontologies for the modeling of SAR missions in wildfire incidents b) to propose and evaluate an LLM-enhanced OE approach. In overall, the main contribution of the work presented in this paper is the analysis of LLMs capabilities to ontology engineering, and the evaluation of the synergy between humans and machines to efficiently represent knowledge, with specific focus in the SAR domain. |
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