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

KEOD is part of IC3K : International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge ManagementInternational audienceNatural 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

Enhancing Interoperability and Inferring Evacuation Priorities in Flood Disaster Response

International audienceDisaster management is a crucial process that aims at limiting the consequences of a natural disaster. Disaster-related data, that are heterogeneous and multi-source, should be shared among different actors involved in the management process to enhance the interoperability. In addition, they can be used for inferring new information that helps in decision making. The evacuation process of flood victims during a flood disaster is critical and should be simple, rapid and efficient to ensure the victims’ safety. In this paper, we present an ontology that allows integrating and sharing flood-related data to various involved actors and updating these data in real time throughout the flood. Furthermore, we propose using the ontology to infer new information representing evacuation priorities of places impacted by the flood using semantic reasoning to assist in the disaster management process. The evaluation results show that it is efficient for enhancing information interoperability as well as for inferring evacuation priorities

An Ontology and a Reasoning Approach for Evacuation in Flood Disaster Response

International audienceManaging flood-related data to assist in the disaster management is a critical process of high importance during a flood disaster. These data are heterogeneous and can be provided from different data sources, and integrating them is a challenging task which allows inferring new information that helps in limiting the consequences of a flood. In this paper, we propose a novel approach that manages heterogeneous flood-related data based on semantic web techniques and helps in limiting the damage caused by floods. We first propose an ontology that is used to formally describe the flood-related data, and we construct our knowledge graph through integrating heterogeneous data using the proposed ontology. Then, we propose a reasoning approach using SHACL rules to infer new information that helps manage the flood disaster or anticipate future events. The experimental evaluations of our proposed approach are conducted on a real case study in flood disaster management with the aim of generating evacuation priorities. The results show that the proposed approach succeeds in managing heterogeneous flood-related data and in generating evacuation priorities in a very short time

An Ontology and a reasoning approach for Evacuation in Flood Disaster Response

International audienceManaging flood-related data to assist in the disaster management is a critical process of high importance during a flood disaster. These data are heterogeneous and can be provided from different data sources, and integrating them is a challenging task which allows to infer new information that helps in limiting the consequences of a flood. In this paper, we propose a novel approach that manages heterogeneous flood-related data based on semantic web techniques and helps in limiting the damage caused by floods. We first propose an ontology that is used to formally describe the flood-related data, and we build our knowledge graph through integrating heterogeneous data using the proposed ontology. Then, we propose a reasoning approach using SHACL rules to infer new information that helps in managing the flood disaster or in anticipating future events. The experimental evaluations of our proposed approach are conducted on a real case study in the frame of flood disaster management with the aim of generatingevacuation priorities. The results show that it succeeds in managing heterogeneous flood-related data and generating evacuation priorities in a very short time