Towards semantic fusion using information quality and the assessment of objects and situations to improve emergency situation awareness

Abstract

Information Fusion is the integration of synergic information to support cognition and high-level processing. Emergency management systems may take advantage of such integration and better support human operators in the development of Situational Awareness (SAW) for decision-making. The critical and dynamic nature of real emergency scenarios impose challenges to reveal, integrate and derive useful information for decision processes. The problem increases when humans are the main source of data, leading to information quality issues, such as imprecision, inconsistency and uncertainty. Current syntactical-only fusion approaches are limited regarding the assessment of situational meaning and human language nuances. Semantic models help to describe and to apply relationships among entities that may be useful for a net centric fusion and Situation Assessment (SA) routines. The objective of this paper is to present advances towards a new semantic fusion approach supported by information quality inferences and semantic web concepts to improve the SA about emergency situations and hence supporting SAW. For such, a new architecture is presented to integrate objects and situation assessment approaches by syntactical and semantic means. A previous fusion approach based on a syntactic integration with quality indexes is used to illustrate the improvements on information fusion results with the semantic models.