Open Access

ARTICLE

Ontology-Based Crime News Semantic Retrieval System

by Fiaz Majeed¹, Afzaal Ahmad¹, Muhammad Awais Hassan², Muhammad Shafiq^3,*, Jin-Ghoo Choi³, Habib Hamam^4,5,6,7

1 Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan
2 Department of Computer Science, University of Engineering & Technology, Lahore, 54890, Pakistan
3 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
4 Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
5 International Institute of Technology and Management, Commune d’Akanda, BP, Libreville, 1989, Gabon
6 School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
7 Spectrum of Knowledge Production & Skills Development, Sfax, 3027, Tunisia

* Corresponding Author: Muhammad Shafiq. Email:

Computers, Materials & Continua 2023, 77(1), 601-614. https://doi.org/10.32604/cmc.2023.036074

Received 16 September 2022; Accepted 18 April 2023; Issue published 31 October 2023

Abstract

Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word mismatches due to context changes and the inevitable semantics of a given word. Therefore, such datasets need to be organized in a structured configuration, with the goal of efficiently manipulating the data while respecting the semantics of the data. An ontological semantic IR system is needed that can find the right investigative information and find important clues to solve criminal cases. The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries. In this paper, we develop an ontology-based semantic IR system that leverages the latest semantic technologies including resource description framework (RDF), semantic protocol and RDF query language (SPARQL), semantic web rule language (SWRL), and web ontology language (OWL). We have conducted two experiments. In the first experiment, we implemented a keyword-based textual IR system using Apache Lucene. In the second experiment, we implemented a semantic system that uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries. The keyword-based system has filtered results with 51% accuracy, while the semantic system has filtered results with 95% accuracy, leading to significant improvements in the field and opening up new horizons for researchers.

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