GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism
In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/7/1/24 |
| _version_ | 1827751283860176896 |
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| author | Reem Qadan Al-Fayez Marwan Al-Tawil Bilal Abu-Salih Zaid Eyadat |
| author_facet | Reem Qadan Al-Fayez Marwan Al-Tawil Bilal Abu-Salih Zaid Eyadat |
| author_sort | Reem Qadan Al-Fayez |
| collection | DOAJ |
| description | In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content. |
| first_indexed | 2024-03-11T06:55:05Z |
| format | Article |
| id | doaj.art-01960df509814b968c956b1f3becbe3a |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-11T06:55:05Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-01960df509814b968c956b1f3becbe3a2023-11-17T09:36:52ZengMDPI AGBig Data and Cognitive Computing2504-22892023-01-01712410.3390/bdcc7010024GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global TerrorismReem Qadan Al-Fayez0Marwan Al-Tawil1Bilal Abu-Salih2Zaid Eyadat3Computer Information Systems Department, King Abdullah II School of Information Technology, The University of Jordan, Amman 11942, JordanComputer Information Systems Department, King Abdullah II School of Information Technology, The University of Jordan, Amman 11942, JordanComputer Science Department, King Abdullah II School of Information Technology, The University of Jordan, Amman 11942, JordanPrince Al Hussein bin Abdullah II School of International Studies, The University of Jordan, Amman 11942, JordanIn recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.https://www.mdpi.com/2504-2289/7/1/24ontologysemantic websocial dataterrorismOWL/RDFknowledge graphs |
| spellingShingle | Reem Qadan Al-Fayez Marwan Al-Tawil Bilal Abu-Salih Zaid Eyadat GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism Big Data and Cognitive Computing ontology semantic web social data terrorism OWL/RDF knowledge graphs |
| title | GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism |
| title_full | GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism |
| title_fullStr | GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism |
| title_full_unstemmed | GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism |
| title_short | GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism |
| title_sort | gtdonto an ontology for organizing and modeling knowledge about global terrorism |
| topic | ontology semantic web social data terrorism OWL/RDF knowledge graphs |
| url | https://www.mdpi.com/2504-2289/7/1/24 |
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