Objects assessment approach using natural language processing and data quality to support emergency situation assessment

Abstract

Situation Awareness (SAW) is a cognitive process that is defined by the perception of relevant elements present in a monitored environment (e.g., people, objects, vehicles, places), the understanding of their meaning (i.e., what they are doing) and the projection of their statuses in the near future. In the domain of emergency management, the data employed to the process of acquisition and maintenance of SAW are provided by several sources, using different formats and different classifications, such as: images from security cameras, reports made to the emergency response center, posts in social networks and several physical sensors, such as: positional, altitude and movement. Data from this sources, if well processed and understood by a specialist, may contribute to the decision-making process, supporting the establishment of emergency response tactics and a better allocation of operational resources. The acquisition of SAW demands the characterization of the ongoing situation. Typically, knowing exactly what is going on demands exhaustive routines of intelligent data assessment. In the emergency management domain, it means to better explore and analyze what the humans say about the events. This paper presents a general architecture that integrates objects and situational assessment for the emergency management domain and a specific process for the objects assessment using natural language processing (NLP) and semantic practices, to better identify relevant elements that may be useful for the situation assessment routines, such as information fusion. Known approaches are limited due to the absence of data quality analysis as part of the process, undesirable when decision makers need to rely on emergency information. Preliminary results of a case study of an intelligent object assessment of a robbery situation reported in Brazilian Portuguese demonstrate the advantages and practical particularities of our solution.