Discovering and evaluating organizational knowledge from textual data: Application to crisis management

Abstract

Crisis management effectiveness relies mainly on the quality of the distributed human organization deployed for saving lives, limiting damage and reducing risks. Organizations set up in this context are not always predefined and static; they could evolve and new forms could emerge since actors, such as volunteers or NGO, could join dynamically to collaborate. To improve crisis resolution effectiveness, it is first important to understand, analyze and evaluate such dynamic organizations in order to adjust crisis management plans and ease coordination among actors. Giving a textual experience feedback from past crisis, the objective of this paper is to discover the organizational structure deployed in the considered crisis and then evaluate it according to a set of criteria. For that purpose, we combine in a coherent framework text and association rule mining for pattern discovery and annotation, and multi-agent system models and techniques for formally building and evaluating organizational structures. We present the OSminer algorithm that discovers association rules based on relevant textual patterns and then builds an organizational structure including three main relations between actors: power, control and coordination. A real-life case study, a flood crisis hitting the south west of France, serves as a basis for testing/experimenting our solution. The organizational structure, discovered in this case study, has 24 actors. Its evaluation indicates its efficiency, but shows that it is neither robust nor flexible. Our findings highlight the potential of our approach to discover and evaluate organizational structures from a text recording interactions between stakeholders in a crisis context.