Organized Crime Structure Modelling for European Law Enforcement Agencies Interoperability through Ontologies

González-Conejero, Jorge; Figueroa, Rebeca Varela; Munoz-Gomez, Juan; Teodoro, Emma

doi:10.1007/978-3-662-45960-7_16

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…This is done in the bottom layer of the ontology, which is called the Concepts Layer. For providing reasoning capabilities, a Reasoning Layer is added as the top layer, importing the classes from the Concepts Layer 17 and adding concepts as \ECQuery" and \EC Result (EC stands for Essential Conditions).…”

Section: Identi¯cation Of Ontologiesmentioning

confidence: 99%

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Kozik

Choraś

Pawlicki

et al. 2020

Vietnam J. Comp. Sci.

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Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.

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Section: Identi¯cation Of Ontologiesmentioning

confidence: 99%

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Kozik

Choraś

Pawlicki

et al. 2020

Vietnam J. Comp. Sci.

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show abstract

“…Ontology is now used widely in many domains like in health [4,5,6], engineering [7,8], software [9,10], commerce [11,12,13], law [14,15], etc. Those Ontologies use different Upper Ontology.…”

Section: Introductionmentioning

confidence: 99%

Ontology Enrichment for Multi-Domain Knowledge and Expertise Representation

Achsan¹,

Suhartanto²,

Wibowo³

2017

DAAAM Proceedings

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Expertise is specialized skills and knowledge. It can be shared or disseminated if stated explicitly using expertise representation. Since people have multi area/domain expertise, we need multi-domain expertise representation. Someone's expertise can be recognized by his/her publication. In this article we proposed a new expertise representation by utilizing bibliography (publication metadata) and annotating ontology (ontology enrichment). The expertise representation is multi-domain that can be integrated semantically as easy as single-domain ontology.

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The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions – MAGNETO Platform Use Case

Kozik

Choraś

Pawlicki

et al. 2019

Computational Collective Intelligence

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Organized Crime Structure Modelling for European Law Enforcement Agencies Interoperability through Ontologies

Cited by 6 publications

References 23 publications

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Common Representational Model and Ontologies for Effective Law Enforcement Solutions

Ontology Enrichment for Multi-Domain Knowledge and Expertise Representation

The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions – MAGNETO Platform Use Case

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