Development of criminal ontologies to enhance situation assessment

Abstract

Situation Awareness (SAW) refers to the level of consciousness that an individual or team holds about a situation. In the field of risk management and criminal data analysis, SAW failures may led human operators to errors in the decision-making process and jeopardize human life, heritage and environment. In this scenario, critical situation assessment processes, which usually involve methods as mining, fusion and others, present opportunities to deliver better information for human reasoning and to assist in the development of SAW. However, on attempting to characterize complex scenarios can lead to poor information representation and expressiveness, which can induce the misinterpretation of data, mainly due to their quality, producing uncertainties. The state-of-the-art on information representation of risk situations and related areas presents approaches with limited usage of the quality of information. In addition, the solutions are limited to syntactic mechanisms for characterizing relations between the information, negatively limiting the assertiveness of the results. Thus, this work aims to present the development of a new approach of semantic information representation of crime situations, more specifically by modeling domain ontologies, instantiated with qualified criminal data. In a case study, real crime information is processed, represented by the new semantic model and consumed by computational inference methods. Results validate the applicability of the produced ontologies on characterizing and inferring robbery and theft situations.