Ontologies play a key role in the Semantic Web, providing the machine-interpretable semantic vocabulary and serving as the knowledge representation and exchange vehicle. On top of ontologies, various functionalities can be supported, such as semantic integration, enrichment and reasoning to either further enhance or enrich them with additional information, or to deduct implicit knowledge out of the already annotated information. This paper presents a holistic semantic model employed within the CONNEXIONs EU-funded project that is aimed at semantically representing and reasoning about all pertinent notions derived from the analysis of high volumes of heterogeneous data with the goal to ultimately improve the capabilities of Law Enforcement Agencies in their fight against crime and terrorism. The proposed model enables the compound of various important aspects that resolve around several sources of information considered important in this context, including online sources that are often adversely exploited, as well information produced by Internet of Thing devices, such as sensors and cameras.