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Automated unsupervised ontology population system applied to crisis management domain

Abstract : As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster.
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Submitted on : Thursday, July 22, 2021 - 10:26:40 AM
Last modification on : Wednesday, July 28, 2021 - 3:13:01 AM

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  • HAL Id : hal-03295469, version 1

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Yohann Chasseray, Anne-Marie Barthe-Delanoë, Stéphane Négny, Jean-Marc Le Lann. Automated unsupervised ontology population system applied to crisis management domain. ISCRAM 2021 - 18th International conference on Information Systems for Crisis Response and Management, May 2021, Balcksburg (online), United States. p.968-981. ⟨hal-03295469⟩

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