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

Abstract

This paper presents an ontology that involves using information from various sources from different disciplines and combining it in order to predict whether a given person is in a radicalization process. The purpose of the ontology is to improve the early detection of radicalization in persons, thereby contributing to increasing the extent to which the unwanted escalation of radicalization processes can be prevented. The ontology combines findings related to existential anxiety that are related to political radicalization with well-known criminal profiles or radicalization findings. The software Protégé, delivered by the technical field at Stanford University, including the SPARQL tab, is used to develop and test the ontology. The testing, which involved five models, showed that the ontology could detect individuals according to “risk profiles” for subjects based on existential anxiety. SPARQL queries showed an average detection probability of 5% including only a risk population and 2% on a whole test population. Testing by using machine learning algorithms proved that inclusion of less than four variables in each model produced unreliable results. This suggest that the Ontology Framework to Facilitate Early Detection of 'Radicalization' (OFEDR) ontology risk model should consist of at least four variables to reach a certain level of reliability. Analysis shows that use of a probability based on an estimated risk of terrorism may produce a gap between the number of subjects who actually have early signs of radicalization and those found by using probability estimates for extremely rare events. It is reasoned that an ontology exists as a world three object in the real world.

Ontology of Mathematical Modeling Based on Interval Data

“…Among the tools used to build and apply the ontology, Protege and OntoStudio are the most commonly used [33,34,50]. Due to their reliability, widespread use, scalability, and extensibility, these tools can also be used in the process of building appropriate ontological models to represent and manage the knowledge they accumulate in the process of mathematical modeling [35,51,52]. However, these tools are difficult to integrate into software and hardware systems, which, in particular, are often used in medicine, where the speed and quality of managing decisions are a priority.…”

“…Within the framework of the proposed approach, the key parameters for the main components of the modeling process are identified, which determine the possibility of generalization and expediency of constructing multifunctional software modules in the development of computer inductive modeling tools based on GMDH [26,31,32]. Since the mentioned approach has a complex structure, which is interpreted using Protege [33][34][35][36] and does not contain applied software-interpreted solutions, its practical use in other approaches to mathematical modeling is not advisable.…”