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Author (up) Antonio De Nicola; Maria Luisa Villani; Francesco Costantino; Andrea Falegnami; Riccardo Patriarca
Title Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework Type Conference Article
Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 79-85
Keywords distributed situational awareness, knowledge fusion, WAx framework, crisis management, cyber-socio-technical systems
Abstract Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios.
Address ENEA; ENEA – CR Casaccia; Sapienza University of Rome; Sapienza University of Rome; Sapienza University of Rome
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes antonio.denicola@enea.it Approved no
Call Number ISCRAM @ idladmin @ Serial 2315
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Author (up) Denis Barcaroli; Alex Coletti; Antonio De Nicola; Antonio Di Pietro; Luigi La Porta; Maurizio Pollino; Vittorio Rosato; Giordano Vicoli; Maria Luisa Villani
Title An Automatic Approach to Qualitative Risk Assessment in Metropolitan Areas Type Conference Article
Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019
Volume Issue Pages
Keywords Risk assessment, geographic information system, conceptual modeling, ontology, computational creativity
Abstract Risk assessment aims at improving prevention and preparedness phases of the crisis management lifecycle.

Qualitative risk assessment of a system is important for risks identification and analysis by the various stakeholders and often requires multi-disciplinary knowledge. We present an automatic approach to qualitative

risk assessment in metropolitan areas using semantic techniques. In particular, users are provided with a computational support to identify and prioritize by relevance risks of city services, through generation of

semantic descriptions of risk situations. This approach is enabled by a software system consisting of: TERMINUS, a domain ontology representing city knowledge; WS-CREAM, a web service implementing risk identification and ranking functions; and CIPCast, a GIS-based Decision Support System with functions of risk

forecast due to natural hazards. Finally we present the results of a preliminary validation of the generated risks concerning some points of interest in two different areas of the city of Rome.
Address ENEA, Italy;Booz Hallen Hamilton, United States
Corporate Author Thesis
Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium
Track T10- Knowledge, Semantics and AI for RISK and CRISIS management Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1886
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Author (up) Rocco Sergio Palermo; Antonio De Nicola
Title A Simulation Framework for Epidemic Spreading in Semantic Social Networks Type Conference Article
Year 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 266-273
Keywords Epidemics; Simulation; Semantic Social Network; Ontology; Crisis
Abstract Epidemic spreading simulation in social networks denotes a set of techniques that allow to assess the temporal evolution and the consequences of a pandemic. They were largely used by governments and International health organizations during the COVID-19 world crisis to decide the appropriate countermeasures to limit the diffusion of the disease. Among them, the existing simulation techniques based on a network model aimed at studying the infectious disease dynamics have a prominent role and are widely adopted. However, even if they leverage the topological structure of a social network, they disregard the intrinsic and individual features of its members. A semantic social network is defined as a structure consisting of interlinking layers, which include a social network layer, to represent people and their relationships and a concept network layer, to represent concepts, their ontological relationships and implicit similarities. Here, we propose a novel epidemic simulation framework that allows to describe a community of people as a semantic social network, to adopt the most commonly used compartmental models for describing epidemic spreading, such as Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Removed (SIR), and to enable semantic reasoning to increase the accuracy of the simulation. Finally, we show how to use the framework to simulate the impact of a pandemic in a community where the job of each member is known in advance.
Address Università Guglielmo Marconi; ENEA
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2416
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