Panagiota Masa

Forest fires, exacerbated by the increasing frequency and intensity of extreme weather events, remain a pressing concern. Developing effective forest fire emergency management systems is paramount to mitigate the impacts of future events. The SAFERS project (Structured Approaches for Forest fire Emergencies in Resilient Societies) addresses this challenge by proposing a modular and comprehensive Emergency Management System (EMS) encompassing all phases of the emergency management cycle. The main backbone of SAFERS is composed of Intelligent Services (ISs) linked with a web-based platform allowing data visualization and end-user interactions. These services integrate diverse data sources, including Earth Observation data, meteorological forecasts, and crowdsourced information. Through advanced technologies such as Artificial Intelligence (AI) algorithms, the ISs enable early warnings, rapid fire detection, propagation prediction, postevent assessment, and monitoring of soil recovery. This work focuses on these services and on the methodologies applied within SAFERS to enhance decision-making capabilities, facilitate effective response strategies, and promote resilience in the face of forest fire emergencies.

Forest fires can have devastating effects on the environment, communities, individuals, economy, and climate change in many countries. During a forest fire crisis, massive amounts of data, such as weather patterns and soil conditions, become available. Efficient management, intelligent integration, and processing the available information in order to extract useful insights and knowledge to facilitate advanced whereas and support human operators and authorities in a real operational scenario is a challenge. In this work, we present ONTO-SAFE, an ontology-based framework for wildfire events, adopting Semantic Web technologies for data integration and infusion of domain and background knowledge. More specifically, the framework creates a unified representation of the available assets, taking into account data generated from different sources, such as sensors, weather forecasts, earth observations, etc. To this end, previously existing ontologies and standards are used, such as Empathi and EmergencyFire ontology, to provide the conceptual model and the necessary level of abstraction in the form of interconnected knowledge graphs to satisfy the modeling requirements. On top of the generated knowledge graphs, a declarative framework extracts facts and higher-level inferred knowledge from asserted data to support users in decision making. In addition, the framework supports the generation of recommendations, such as sharing important wildfire information with citizens and professionals, that can be adjusted based on user-defined factors and the current disaster risk management phase.

Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendations.

Every year, thousands of forest fires throughout the world cause disasters. One of the most critical challenges during a wildfire disaster is the effective management of heterogeneous information relative to the crisis to support human operators and authorities. Towards addressing this challenge, this paper presents an ontology-based framework for data representation and interlinking of wildfire events that are being used to foster advanced reasoning, situational awareness and interpretation for decision support. More specifically, we illustrate the capabilities of the ONTO-SAFE ontology to symbolically model contextual information in the domain, addressing application and user requirements promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of constraints and rules to recognize patterns and situations of interest based on domain knowledge, assisting end-users in taking informed decisions and facilitating advanced decision-making.