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In daily police practice, forensic investigation of criminal cases is mainly based on manual work and the experience of individual forensic experts, using basic storage and data processing technologies. However, an individual criminal case does not only consist of the actual offence, but also of a variety of different aspects involved. For example, in order to solve a financial criminal case, an investigator has to find interrelations between different case entities as well as to other cases. The required information about these different entities is often stored in various databases and mostly requires to be manually requested and processed by forensic investigators. We propose the application of semantic technologies to the domain of forensic investigations at the example of financial crimes. Such combination allows for modelling specific case entities and their interrelations within and between cases. As a result, an explorative search of connections between case entities in the scope of an investigation as well as an automated derivation of conclusions from an established fact base is enabled. The proposed model is presented in the form of a crime field ontology, based on different types of knowledge obtained from three individual sources: open source intelligence, forensic investigators and captive interviews of detained criminals. The modelled crime field ontology is illustrated at two examples using the well known crime type of explosive attack on ATM and the potentially upcoming crime type data theft by NFC crowd skimming. Of these criminal modi operandi, anonymized fictional are modelled, visualized and exploratively searched. Modelled case entities include modi operandi, events, actors, resources, exploited weaknesses as well as flows of money, data and know how. The potential exploration of interrelations between the different case entities of such examples is illustrated in the scope of a fictitious investigation, highlighting the potential of the approach.
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