A scheme of criminal law knowledge acquisition using ontology

Osathitporn, Pongpanut; Soonthornphisaj, Nuanwan; Vatanawood, Wiwat

doi:10.1109/snpd.2017.8022696

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Osathitporn et al [31], present an ontology for the criminal legal domain in Thailand. The objective of this ontology is to represent legal elements in the law domain, and its main artefacts are crime and justification.…”

Section: Ontologies In Forensic Analysesmentioning

confidence: 99%

Crime Intelligence from Social Media Using CISMO

Elezaj

Yayilgan

Ahmed

et al. 2020

Proceedings of Fifth International Congress on Information and Communication Technology

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Nowadays, Online Social Networks (OSNs) are being used as a hosting ground for criminal activities, and the legal enforcement agencies (LEAs) are struggling to process and analyse the huge amount of data coming from these sources. OSNs generate a huge massive volume of unstructured data making difficult for the LEAs to 'patrol the facts' and to gather intelligence in order to provide it to the legal domain. There is no ontology model, among those found in literature, that allows to exhaustively describe all the aspects of crime investigation targeting data integration, information sharing, collection and preservation of digital evidences by using biometric features, and query answering. To bridge this gap, this paper presents an extended version of our earlier SMONT ontology, called CISMO as a semantic tool suitable for gathering digital evidence from OSNs helping LEAs to develop new investigative systems to counter the threat of different crimes. The new version introduces the core concepts related to crime cases in the police repositories, biometric data and digital evidences collected by OSNs, making possible for LEAs to classify crimes, investigate hidden crime patterns or predict future crime patterns. CISMO is more concise and has a richer concept knowledge base compared with the previous version SMONT. We prove the effectiveness of CISMO in a case study covering some general aspects in criminal cases in OSNs, demonstrating how this semantic approach can help LEAs to gather knowledge for crime investigation using natural language processing and machine learning to process messages shared in online platforms and also applying reasoning rules, as semantic inferences.

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Section: Ontologies In Forensic Analysesmentioning

confidence: 99%

Crime Intelligence from Social Media Using CISMO

Elezaj

Yayilgan

Ahmed

et al. 2020

Proceedings of Fifth International Congress on Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…The Semantic Web provides computer interpretable models [6], which are useful for representing semantic relations between domain concepts. Law and security concepts [3,[18][19][20], for instance, can be better interpreted (by human and computers) through the formal representation using ontologies. We chose to use an ontology to describe aspects related to the language used to commit criminal acts.…”

Section: Introductionmentioning

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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A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering

Fawei

Pan

Kollingbaum

et al. 2018

Lecture Notes in Computer Science

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The Internet and the development of the semantic web have created the opportunity to provide structured legal data on the web. However, most legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language question. One approach is to develop systems of legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable legal rules. The purpose of the ontology is to provide reasoning support to an legal question answering tool that determines entailment between a pair of texts, one known as the Background information (Bg) and the other Question statement (Q), whether Bg entails Q based on the application of the law. The key contribution of this paper is a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).

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A scheme of criminal law knowledge acquisition using ontology

Cited by 6 publications

References 6 publications

Crime Intelligence from Social Media Using CISMO

Crime Intelligence from Social Media Using CISMO

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

A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering

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