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

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


Nguyen, Ngoc Thanh (Ed.):
Computational Collective Intelligence. 11th International Conference, ICCCI 2019. Proceedings. Pt.2 : Hendaye, France, September 4-6, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Artificial Intelligence 11684)
ISBN: 978-3-030-28373-5 (Print)
ISBN: 978-3-030-28374-2 (Online)
International Conference on Computational Collective Intelligence (ICCCI) <11, 2019, Hendaye>
Conference Paper
Fraunhofer IOSB ()
Ontology; artificial intelligence; Common Representational Model; semantic interoperability; correlation

Every single day more and more organizations face the challenge of finding a way to support their conduct with data. The flooding amounts of data currently available vastly outweigh human capabilities, thus Big Data processing becomes a pressing issue. This problem is especially prevailing for Law Enforcement Agencies (LEAs), where massive amounts of critical data are collected from heterogenous sources, often by various entities in different countries. Ontologies have been developed into a predominant technique for establishing semantic interoperability among heterogeneous systems which transact information. In this paper we propose the Magneto ontology – a solution built as a crucial part of the Magneto project. It has been developed on top of well-established ontologies dealing with people, events and security incidents, bearing in mind the heterogenous nature of the myriad of data sources as the starting point. Examples of the building blocks, a classification of the sources of data, an overview of the application in a specific use scenario and a discussion on the future use of the ontology will be given.