# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 2 Background and Related Work
### Ontologies and Ontology-Based Systems for Emergencies  
Ontology-based emergency management systems are frequently utilized to assist decision makers and first responders manage disasters in a more effective way. Thus, an effective emergency management system provides a solution to the problem of the generated heterogeneous data and is widely used in various domains. Numerous research investigations have been conducted as a result, and they are discussed in this section. First of all, SoKNOS [22] is an ontology-based emergency management system that aims to support users with the ability to enhance their accessibility of the appropriate information at the appropriate moment. The system incorporates a range of data sources, including real-time sensor data and conventional emergency management data sources to provide a comprehensive view of the emergency situation. One of the key benefits of the SoKNOS system is the ability to define relationships and patterns between resources and damages and identify anomalies in the data. The system can effectively detect crucial information and inform responders of potential hazards by using ontology to express the underlying knowledge and connections between concepts. Numerous use cases evaluated the SoKNOS system and illustrated how the use of ontologies and semantic technologies may increase the functionality and adaptability of emergency management systems. The evaluation of the SoKNOS ontologies was conducted with the assistance of end users via relative workshops to consolidate and validate the engineered ontologies. Another highly relevant approach is Empathi ontology [23]. This ontology conceptualizes the core concepts that define the field of the management of emergencies and hazard crisis mitigation. Empathi ontology was utilized for disaster concept recognition in a social media conversation to assist in crisis management and hazard situational awareness during emergencies. The Empathi ontology was created with Semantic Web language (OWL) and incorporates connections with other significant ontologies including Friend of a Friend (FOAF) ontology [24], SIOC [25], DCMI metadata terms vocabulary [26], and LODE [27]. Age group, event, hazard type, hazard phase, impact, status, and other fundamental concepts are included in the 423 classes and 338 relations that constitute the Empathi ontology and some of them such as fire, impact, location, report, etc., are effectively reused in the construction of ONTO-SAFE ontology as described in Section 4. For the quality evaluation of Empathi ontology, three dominant criteria were used: structural, lexical, and semantic relational evaluation using a human judge. The main objective of Empathi ontology is to map the words of social media content to ontology classes and increase the understanding of the concept of this text in order to enhance knowledge in the emergency management situation. In this way, users are able to categorize communications as relevant or irrelevant to a emergency and extract pertinent information from unstructured social media content. In addition, EmergencyFire ontology [28] is an ontology that describes emergency response protocols for fire emergencies in buildings. The main objective of the EmergencyFire ontology is to enhance information integration and supports knowledge sharing in order to assist organizations to quickly respond in a building fire crisis. This ontology contains103 classes, 67 subclasses, 34 object properties, 26 datatype properties, 34 instances, and 21 swrl rules, and the ontology's evaluation process was conducted by experts who evaluated the classes, properties, taxonomy, rules, and instances of the ontology. Some potential classes were found in EmergencyFire ontology such as fire, incident, weather conditions, and communication, which cover needs that also exist for the design of the ONTO-SAFE ontology as represented in Section 4. The applicability of the EmergencyFire ontology facilitates sharing of information and helps the emergency system to automatically reason through inferences.