Poster paper for the 2017 International Conference on Knowledge Management. This paper explores the use of artificial intelligence in business to improve organizational effectiveness.

Managing 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 generating evacuation priorities. The results show that it succeeds in managing heterogeneous flood-related data and generating evacuation priorities in a very short time.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. The conference was among the first post-pandemic face to face conferences, and the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency.

Identifying support from Knowledge Management (KM) frameworks and Risk Management to build collective intelligence for handling systemic crises, triggered by the Corona virus disease 2019 (Covid-19)