# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 4 Ontology Overview  
The purpose of ontology development is to facilitate knowledge organization and sharing among different components in order to address hazardous situations during wildfires. It is critical to define significant entities, concepts, and processes in order to construct a conceptual framework for organizing elements in a specific domain. Ontology is a useful tool because it uses classes, properties, and instances to support data modeling, integration, and reasoning. By leveraging these mechanisms, ontology enhances the management and analysis of the resulting information, providing a structured approach to handle and make sense of the data. In our previous work [9], we present the initial ontology-based approach which is a lightweight ontology able to semantically describe knowledge for the elements encompassing a wildfire disaster, such as incidents, impacts, the corresponding weather conditions, data derived from human and earth observations, and the missions and interconnections among various services involved. Figure 2 represents the main classes of the initial ontology which consist of the classes Fire, Impact, Location, Incident, WeatherConditionParameter, WeatherForecast, Dataset, Mission, MediaItem, Detection, Alert. The class _Fire_ is reused from Empathi and EmergencyFire ontologies and depicts the different types of fires, e.g., forest fire, bushfire, wildland fire, or rural fire, etc, whereas the _Impact_ class, which is reused from Empathi and  
Figure 1: ONTO-SAFE architecture. BeAWARE ontologies, represents the impact of wildfire disasters (i.e., injuries, damage to properties, fire propagation, etc. ), which are related to incidents (via class _Incident_). Each potential or existing fire is identified by a specific location (via class _Location_). Every location has meteorological conditions (e.g., temperature, wind speed, and humidity), which are represented by the class _WeatherConditionParameter_, as well as a prediction of the weather in the near future, which is represented via the class _WeatherForecast_. In addition, in every fire incident, data coming from various sources including satellites, sensors, and social media communications (e.g., Twitter, chatbot, etc.) are produced and presented via class _Dataset_. The class _MediaItem_, which is reused also from BeAWARE ontology, represents an item of analyzed data produced during the crisis, such as text, images, videos, and audio. From this data analysis fire detection (via class _Detection_) or alerts (via class _Alert_) can be produced. End-user participation in the creation of the ONTO-SAFE ontology is thought to be highly beneficial because it helps us to better understand end-user needs and expectations. Furthermore, the International User Requirements Workshop (IURW) and the online survey were used to identify and prioritize requirements. These two techniques were used to obtain comments from end-users such as police, firefighters, and wildfire managers, model the requirements through the competency questions [36] and transform them into semantic entities by providing the specification of the ONTO-SAFE ontological framework. The conduct of an extra survey during the first pilots of the project was deemed necessary to collect additional user needs. For this reason, additional classes and properties were added during the implementation of the work in order to better comply with the user requirements. Table 1 presents these additions to competency questions: