Ontology-driven automated reasoning about property crimes

Abstract

The classification of police reports according to the typification of the criminal act described in them is not an easy task. The reports are written in natural language and often present missing, imprecise, or even inconsistent information, or lack sufficient details to make a clear decision. Focusing on property crimes, the aim of this work is to assist judges in this classification process by automatically extracting information from police reports and producing a list of possible classifications of crimes accompanied by a degree of confidence in each of them. The work follows the design science research methodology, developing a tool as an artifact. The proposal uses information extraction techniques to obtain the data from the reports, guided by an ontology developed for the Spanish legal system on property crimes. Probabilistic inference mechanisms are used to select the set of articles of the law that could apply to a given case, even when the evidence does not allow an unambiguous identification. The proposal has been empirically validated in a real environment with judges and prosecutors. The results show that the proposal is feasible and usable, and could be effective in assisting judges to classify property crime reports.