Title: SMONT: an ontology for crime solving through social media

Authors: Edlira Kalemi; Sule Yildirim-Yayilgan; Elton Domnori; Ogerta Elezaj

Addresses: Process & Information Systems Engineering Research Centre, University of Surrey, Guildford, Surrey, GU2 7XH, UK ' Department of Information Security and Communication Technology, Norwegian University of Science and Technology, 2815 Gjøvik, Norway ' Department of Computer Engineering, Epoka University, 1039 Tirana, Albania ' Department of Mathematics, Statistics and Applied Informatics, University of Tirana, 1000 Tirana, Albania

Abstract: There are numerous social networks such as Facebook, LinkedIn, Google Plus and Twitter whose data sources are becoming larger every day holding an abundance of valuable information. Among these data, digital crime evidence can be collected from online social networks (OSNs) for crime detection and further analysis. This paper describes the SMONT ontology which has been developed to give support to the process of crime investigation and prevention. The SMONT ontology covers specific data about the crime, digital evidence obtained from OSNs, information archived from police entities, and also details related to people or events which may bring the authorities closer to crime case solving. It is possible to benefit from the ontology in different ways like: intelligence gathering; reasoning over the data; smarter searches and comparisons; open data publication purposes; and for the overall management of the crime solving and prevention process.

Keywords: ontology; online social networks; crime; digital evidence.

DOI: 10.1504/IJMSO.2017.090756

International Journal of Metadata, Semantics and Ontologies, 2017 Vol.12 No.2/3, pp.71 - 81

Received: 14 Oct 2016
Accepted: 07 Sep 2017

Published online: 27 Mar 2018 *

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