ONTOLOGICAL MODELING OF INFORMATION DATA OF DIGITAL CRIMINAL CRIME

Abstract

In the article, an ontological model of information data of a digital criminal offense is formed and researched. Ontological modeling made it possible to conceptualize knowledge and effectively overcome the problems of insufficient structure, ambiguity and inconsistency of data and knowledge in the field of digital forensics. On the basis of the conducted classification, five main classes (Digital Crime, Digital Traces, Types of Crimes, Criminal and Criminal Liability) were identified, which include multiple user and non-user instances, including relevant articles of the Criminal Code of Ukraine and international law. The user creates instances of three classes: Digital Crime, Digital Traces, and Criminal. They contain personal information about digital crime and are the main data of the user part of the ontological model as a knowledge base. The Crime Types and Criminal Liability classes are non-user and can only be modified by model support specialists. The ontology model is implemented in Protege in the OWL language, which is an informal standard for creating and sharing ontologies. Of the selected seven relationships between entities, only three are entered into the ontology by the user, the others are formed automatically based on the developed SWRL rules. Using the SPARQL query language, real-time information search, filtering, and analysis patterns are provided to help discover complex relationships between objects and generate new ontological knowledge. The results of the study highlight the importance of ontology modeling in the field of digital forensics and how SPARQL queries can be used to improve data processing, analysis and understanding of knowledge in this field.