Towards Designing a Knowledge Graph-Based Framework for Investigating and Preventing Crime on Online Social Networks

Abstract

Online Social Networks (OSNs) have fundamentally and permanently altered the arena of digital and classical crime. Recently, law enforcement agencies (LEAs) have been using OSNs as a data source to collect Open Source Intelligence for fighting and preventing crime. However, most existing technological developments for LEAs to fight and prevent crime rely on conventional database technology, which poses problems. As social network usage is increasing rapidly, storing and querying data for information retrieval is critical because of the characteristics of social networks, such as unstructured nature, high volumes, velocity, and data interconnectivity. This paper presents a knowledge graph-based framework, an outline of a framework designed to support crime investigators solve and prevent crime, from data collection to inferring digital evidence admissible in court. The main component of the proposed framework is a hybrid ontology linked to a graph database, which provides LEAs with the possibility to process unstructured data and identify hidden patterns and relationships in the interconnected data of OSNs.

An Ontological Framework to Facilitate Early Detection of ‘Radicalization’ (OFEDR)—A Three World Perspective

“…However, the effect of EA is documented in a considerable number of studies (500 reported in 2014, and many thereafter). Ontologies can be used as a tool to investigate and prevent crime [ 107 ]. However, the literature search undertaken related to this work showed that radicalization, especially terrorism, is mainly investigated at a higher policy level [ 108 ].…”