SMONT: an ontology for crime solving through social media

摘要

There are numerous social networks such as Facebook, LinkedIn, Google Plus and Twitter whose data sources are becoming larger every day holding an abundance of valuable information. Among these data, digital crime evidence can be collected from online social networks (OSNs) for crime detection and further analysis. This paper describes the SMONT ontology which has been developed to give support to the process of crime investigation and prevention. The SMONT ontology covers specific data about the crime, digital evidence obtained from OSNs, information archived from police entities, and also details related to people or events which may bring the authorities closer to crime case solving. It is possible to benefit from the ontology in different ways like: intelligence gathering; reasoning over the data; smarter searches and comparisons; open data publication purposes; and for the overall management of the crime solving and prevention process.