An ontology supported hybrid approach for recommendation in emergency situations

Mehla, Sonia; Jain, Sarika

doi:10.1007/s12243-020-00786-z

Cited by 7 publications

(10 citation statements)

References 25 publications

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“…Recommender systems based on a knowledge base represented by means of ontologies have been widely developed for various domains, including e-commerce [14], education, and engineering [15], remarkably, in emerging domain crisis management [16,17]. We detail in the next section, recommender systems and their applications for suggesting relevant resources in crisis management.…”

Section: Ontologiesmentioning

confidence: 99%

Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

Ducange,

Fazzolari,

Marcelloni

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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In the context of the evacuation of populations, some citizens/volunteers may want and be able to participate in the evacuation of populations in difficulty by coming to lend a hand to emergency/evacuation vehicles with their own vehicles. One way of framing these impulses of solidarity would be to be able to list in real-time the citizens/volunteers available with their vehicles (land, sea, air, etc.), to be able to geolocate them according to the risk areas to be evacuated, and adding them to the evacuation/rescue vehicles. Because it is difficult to propose an effective real-time operational system on the field in a real crisis situation, in this work, we propose to add a module for recommending driver/vehicle pairs (with their specificities) to a system of crisis management simulation. To do that, we chose to model and develop an ontology-supported constraint-based recommender system for crisis management simulations.

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Section: Ontologiesmentioning

confidence: 99%

Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

Ducange,

Fazzolari,

Marcelloni

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…23 In particular, they have gained significant attention in the emerging domain of crisis management. 5,6 Jain et al 5 design a recommender system that can perform case-based reasoning and rule-based reasoning supported by ontologies. This system can recommend a list of actions that help to reduce damages caused by earthquakes.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…The recommender systems presented in previous works [5][6][7] aim to assist decision-makers in the optimal distribution of public resources, such as ambulances and fire trucks. However, we argue that public resources are sometimes limited and may not be located to serve all affected areas during a crisis.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…This system can recommend a list of actions that help to reduce damages caused by earthquakes. Mehla and Jain 6 present an ontology-supported recommender system that helps decision-makers to respond effectively during emergencies; recommendations are generated by combining case-based and rule-based reasoning like Jain et al 5 Zhang et al 7 applied rule-based reasoning using Semantic Web Rule Language (SWRL) to help manage crises caused by meteorological disasters.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, a boat owner with a capacity of six passengers could help evacuate five people in the event of a flood. Unfortunately, existing research on crisis management (e.g., evacuation simulation) 1,[3][4][5][6][7][8] has primarily focused on the use of public resources, such as ambulances 8,9 and fire trucks. 5 However, in some cases, these resources may not be available due to high demand or the remote location of the affected area.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based crisis simulation system for population sheltering management

Zhong,

Ngoc,

Negre

et al. 2023

SIMULATION

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Climate change has led to an increase in the frequency and intensity of natural disasters, necessitating the development of efficient crisis management strategies for population sheltering. However, existing research on this topic primarily focuses on the use of public resources such as ambulances and fire trucks, which may sometimes be insufficient due to high demand and impacted locations, worsening the shortage of resources. This research introduces an ontology-based crisis simulation system for population sheltering management that focuses on the integration and distribution of citizen–volunteer drivers/vehicles into the evacuation process. Recognizing the limitations of public resources in current crisis management models, our approach incorporates citizen resources to enhance overall evacuation capacity. We develop an ontology to standardize crisis management knowledge, frame vehicle distribution as a recommendation problem, and design a simulation module incorporating a constraint-based recommender system. The proposed scenario illustrates how the simulation system can recommend citizen resources during crisis situations by considering the constraints to be satisfied. With our system, we aim at helping stakeholders to be prepared for various disaster scenarios: optimizing resource allocation and reducing time to make decisions by decision-makers.

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Ontology for Data Analytics

Şentürk

2021

Smart Connected World

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An ontology supported hybrid approach for recommendation in emergency situations

Cited by 7 publications

References 25 publications

Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

Ontology-based crisis simulation system for population sheltering management

Ontology for Data Analytics

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