SMoNt: An ontology for crime solving through social media

作     者：Kalemi, Edlira Yildirim-Yayilgan, Sule Domnori, Elton Elezaj, Ogerta 

作者机构：Process and Information Systems Engineering Research Centre University of Surrey Guildford SurreyGU2 7XH United Kingdom Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik2815 Norway Department of Computer Engineering Epoka University Tirana1039 Albania Department of Mathematics Statistics and Applied Informatics University of Tirana Tirana1000 Albania 

出 版 物：《International Journal of Metadata, Semantics and Ontologies》 (Int. J. Metadata Semant. Ontol.)

年 卷 期：2017年第12卷第2-3期

页      面：71-81页

核心收录：

学科分类：0303[法学-社会学] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Ontology 

摘      要：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. Copyright © 2017 Inderscience Enterprises Ltd.