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The ARK platform: enabling risk management through semantic web technologies

Crotti Junior, Ademar orcid logoORCID: 0000-0003-1025-9262, Basereh, Maryam, Abgaz, Yalemisew orcid logoORCID: 0000-0002-3887-5342, Liang, Junli, Duda, Natalia, McDonald, Nick and Brennan, Rob orcid logoORCID: 0000-0001-8236-362X (2020) The ARK platform: enabling risk management through semantic web technologies. In: 11th International Conference on Biomedical Ontologies (ICBO 2020), 17 Sept 2020, Bolzano, Italy (Online).

Abstract
This paper describes the Access Risk Knowledge (ARK) platform and ontologies for socio-technical risk analysis using the Cube methodology. Linked Data is used in ARK to integrate qualitative clinical risk management data with quantitative operational data and analytics. This required the development of a novel clinical safety management taxonomy to annotate qualitative risk data and make it more amenable to automated analysis. The platform is complemented by other two ontologies that support structured data capture for the Cube sociotechnical analysis methodology developed by organisational psychologists at Trinity College Dublin. The ARK platform development and trials have shown the benefits of a Semantic Web approach to flexibly support data integration, making qualitative data machine readable and building dynamic, high-usability web applications applied to clinical risk management. The main results so far are a self-annotated, standards-based taxonomy for risk and safety management expressed in the W3C’s standard Simple Knowledge Organisation System (SKOS) and a Cube data capture, curation and analysis platform for clinical risk management domain experts. The paper describes the ontologies and their development process, our initial clinical safety management use case and lessons learned from the application of ARK to real-world use cases. This work has shown the potential for using Linked Data to integrate operational and safety data into a unified information space supporting more continuous, adaptive and predictive clinical risk management.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:In conjunction with the10th Workshop on Ontologies and Data in Life Sciences (ODLS) and part of the Bolzano Summer of Knowledge (BoSK 2020)
Uncontrolled Keywords:ARK Platform; Organisational Change; Risk Management
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 11th International Conference on Biomedical Ontologies (ICBO). 2807. CEUR-WS.
Publisher:CEUR-WS
Official URL:http://ceur-ws.org/Vol-2807/paperM.pdf
Copyright Information:© The Authors (CC-BY-4.0)
Funders:Enterprise Ireland under Grant Agreement No. CF 2018-2012, Science Foundation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant # 13/RC/2106
ID Code:25394
Deposited On:18 Jan 2021 14:23 by Vidatum Academic . Last Modified 10 Feb 2021 13:29
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