SMONT: an ontology for crime solving through social media

an article by Edlira Kalemi (University of Surry, Guildford, UK), Sule Yildirim-Yayilgan (Norwegian University of Science and Technology, Gjøvik, Norway), Elton Domnori (Epoka University, Tirana, Albania) and Ogerta Elezaj (University of Tirana, Albania) published in International Journal of Metadata, Semantics and Ontologies Volume 12 Number 2/3 (2017)

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.

---

Toward an adaptive semantic search mechanism for the “Web of Things”

an article by Benoit Christophe, Vincent Verdot and Vincent Toubiana (Bell Labs Research, Alcatel-Lucent Bell Labs, France) published in International Journal of Semantic Computing Volume 5 Issue 4 (2011)

Abstract

With the proliferation of connected devices and the widespread adoption of the Web, ubiquitous computing success has recently been brought into the fashion of an emergent paradigm called the “Web of Things”, where Web-enabled objects are offered through interconnected smart spaces. While some predict a near future with billions of Web-enabled objects, the success of this vision now depends on the creation of efficient processes and the availability of tools enabling users or applications to find connected objects matching a set of requirements (and expectations).

We present an on-going work that aims to develop a search process dedicated to the “Web of Things” and that relies on three contributions.

Firstly, the creation and use of semantic profiles for connected objects.
Secondly, the establishment of similarities between semantic profiles of different connected objects to gather them into clusters.
Thirdly, the computation of a score associating a “context of search” to an incoming request and enabling the selection of the most appropriate search algorithms, involving either probabilistic or precise reasoning.

---