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Communication Dans Un Congrès Année : 2022

Inferring New Information from a Knowledge Graph in Crisis Management: A Case Study

Résumé

Natural crises are dangerous events that can threaten lives and lead to severe damages. Crisis-related data can be heterogeneous and be provided from multiple data sources. These data can be formally described using ontologies and then integrated and structured forming knowledge graphs. Inferring new information from knowledge graphs can strongly assist in the various phases of the crisis management process. Different approaches exist in the literature for inferring new information from knowledge graphs. In this paper, we present a case study of a flood crisis where we discuss three approaches for inferring flood-related information, and we experimentally evaluate these approaches using real flood-related data and synthetic data for further analysis. We discuss the interest of using each of these approaches and detail its advantages as well as its limitations.

Dates et versions

hal-03940533 , version 1 (16-01-2023)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Julie Bu Daher, Tom Huygue, Nathalie Jane Hernandez, Patricia Stolf. Inferring New Information from a Knowledge Graph in Crisis Management: A Case Study. 14th International Conference on Knowledge Engineering and Ontology Development (KEOD 2022), Oct 2022, Valletta, Malta. pp.43-54, ⟨10.5220/0011511200003335⟩. ⟨hal-03940533⟩
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