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Common Representational Model and Ontologies for Effective Law Enforcement Solutions

 
: Kozik, Rafal; Choras, Michal; Pawlicki, Marek; Holubowicz, Witold; Pallmer, Dirk; Müller, Wilmuth; Behmer, Ernst-Josef; Loumiotis, Ioannis; Demestichas, Konstantinos; Horincar, Roxanna; Laudy, Claire; Faure, David

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Volltext ()

Vietnam journal of computer science 7 (2020), Nr.1, S.1-18
ISSN: 2196-8888 (Print)
ISSN: 2196-8896 (Online)
European Commission EC
H2020; 786629; MAGNETO
Multimedia Analysis and Correlation Engine for Organised Crime Prevention and Investigation
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
Ontology; artificial intelligence; Common Representational Model; semantic interoperability; correlation

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-585564.html