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DOI: 10.1055/s-0041-1735621
Towards the Representation of Network Assets in Health Care Environments Using Ontologies
Funding The research work presented in this article has been supported by the European Commission under the Horizon 2020 Programme, through funding of the CUREX project (G.A. n 826404).![](https://www.thieme-connect.de/media/10.1055-s-00035037/2021S02/lookinside/thumbnails/10-1055-s-0041-1735621_21020004-1.jpg)
Abstract
Objectives The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.
Methods Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.
Results As a result of this work, the “Software Defined Networking Description Language—CUREX Asset Discovery Tool Ontology” (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc.
Conclusion The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.
Publication History
Received: 14 April 2021
Accepted: 20 July 2021
Article published online:
05 October 2021
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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References
- 1 Sørensen C, de Reuver M, Basole RC. Mobile Platforms and Ecosystems. J Inf Technol 2015; 30 (03) 195-197
- 2 Tully J, Selzer J, Phillips JP, O'Connor P, Dameff C. Healthcare challenges in the era of cybersecurity. Health Secur 2020; 18 (03) 228-231
- 3 Critical Infrastructures and Services. Accessed April 13, 2021 at: https://www.enisa.europa.eu/topics/critical-information-infrastructures-and-services
- 4 Council Directive 2008/114/EC of 8 December 2008 on the Identification and Designation of European Critical Infrastructures and the Assessment of the Need to Improve Their Protection (Text with EEA Relevance). Vol OJ L.; 2008. Accessed April 13, 2021 at: http://data.europa.eu/eli/dir/2008/114/oj/eng
- 5 Shi F, Li Q, Zhu T, Ning H. A survey of data semantization in internet of things. Sensors (Basel) 2018; 18 (01) E313
- 6 Kaur K, Rani R. Managing data in healthcare information systems: many models, one solution. Computer 2015; 48 (03) 52-59
- 7 Chen P-T, Lin C-L, Wu W-N. Big data management in healthcare: adoption challenges and implications. Int J Inf Manage 2020; 53: 102078
- 8 Kolias VD, Stoitsis J, Golemati S, Nikita KS. Utilizing semantic web technologies in healthcare. In: Koutsouris D-D, Lazakidou AA. eds. Concepts and Trends in Healthcare Information Systems. Annals of Information Systems. Springer International Publishing; 2014: 9-19
- 9 Hammad R, Barhoush M, Abed-Alguni BH. A semantic-based approach for managing healthcare big data: a survey. J Healthc Eng 2020; 2020: 8865808
- 10 Kim DJ, Hebeler J, Yoon V, Davis F. Exploring determinants of semantic web technology adoption from IT professionals' perspective: industry competition, organization innovativeness, and data management capability. Comput Human Behav 2018; 86: 18-33
- 11 Guarino N. Formal Ontologies and Information Systems. IOS Press; 1998
- 12 Guarino N, Oberle D, Staab S. What is an ontology?. In: Staab S, Studer R. eds. Handbook on Ontologies. International Handbooks on Information Systems. Berlin, Heidelberg: Springer; 2009: 1-17
- 13 RDF - Semantic Web Standards. Published November 24, 2019. Accessed November 24, 2019 at: https://www.w3.org/RDF/
- 14 SPARQL Query Language for RDF. Published November 24; 2019. Accessed November 24, 2019 at: https://www.w3.org/TR/rdf-sparql-query/
- 15 Wilkinson MD, Dumontier M, Aalbersberg IJ. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018
- 16 OpenLink Virtuoso Universal Server Documentation. Published November 25, 2019. Accessed November 25, 2019 at: http://docs.openlinksw.com/virtuoso/
- 17 Antoniou G, van Harmelen F. Web Ontology Language: OWL. In: Staab S, Studer R. eds. Handbook on Ontologies. International Handbooks on Information Systems. Berlin, Heidelberg: Springer; 2004: 67-92
- 18 Zhou Q, Gray AJG, McLaughlin S. ToCo: An ontology for representing hybrid telecommunication networks. In: Hitzler P, Fernández M, Janowicz K. et al, eds. The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019. Series: Lecture Notes in Computer Science (11503). Springer; 507-522
- 19 Sikos LF, Stumptner M, Mayer W, Howard C, Voigt S, Philp D. Representing network knowledge using provenance-aware formalisms for cyber-situational awareness. Procedia Comput Sci 2018; 126: 29-38
- 20 Mozzaquatro BA, Agostinho C, Goncalves D, Martins J, Jardim-Goncalves R. an ontology-based cybersecurity framework for the internet of things. Sensors (Basel) 2018; 18 (09) E3053
- 21 Herzog A, Shahmehri N, Duma C. An ontology of information security. IJISP 2007; 1: 1-23
- 22 Voigt S, Howard C, Philp D, Penny C. Representing and reasoning about logical network topologies. In: Croitoru M, Marquis P, Rudolph S, Stapleton G. eds. Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science.. Springer International Publishing; 2018: 73-83
- 23 Intelligent Systems Group. Software defined networking description language. Accessed March 5, 2021 at: http://www.gsi.upm.es/ontologies/sdndl/
- 24 Grosso P, Dijkstra F, Van der Ham J, Laat C. Network description language–semantic web for hybrid networks. Proceedings of TERENA Networking Conference 2007. Kongens Lyngby, Copenhagen, Denmark
- 25 Ghijsen M, van der Ham J, Grosso P, de Laat C. Towards an infrastructure description language for modeling computing infrastructures. Paper presented at: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications; 2012: 207-214
- 26 van der Ham J, Dijkstra F, Lapacz R, Brown A. The network markup language (nml) a standardized network topology abstraction for inter-domain and cross-layer network applications. Paper presented at: Proceedings of the 13th TERENA Networking Conference, Maastricht, Netherlands; 2013
- 27 Batsakis S, Petrakis EGM, Tachmazidis I, Antoniou G. Temporal representation and reasoning in OWL 2. Semant Web 2017; 8 (06) 981-1000