An Ontological Approach to the Three-Phase Method of Imposing Penalties in the Brazilian Criminal Code

Rodrigues, Cleyton Mário de Oliveira; Oliveira, Ítalo

doi:10.1109/bracis.2017.21

Cited by 1 publication

(1 citation statement)

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“…We defined the crime domain as the scope of the ontology, and performed the correlation between expressions and terms used by people in criminal activities. For reuse purposes, we evaluated the top-level ontologies SUMO, UFO-B, and DOLCE, as proposed in the studies [81][82][83]. However, we chose not to use them due to the particularities of the crime domain.…”

Section: Ontology Engineering Processmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Ontology Engineering Processmentioning

confidence: 99%