From ontology to knowledge graph with agile methods: the case of COVID-19 CODO knowledge graph
International Journal of Web Information Systems
ISSN: 1744-0084
Article publication date: 5 October 2022
Issue publication date: 12 December 2022
Abstract
Purpose
The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph.
Design/methodology/approach
The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation.
Findings
The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.”
Originality/value
This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.
Keywords
Acknowledgements
This work is executed under the research project entitled “Integrated and Unified Data Model for Publication and Sharing of prolonged pandemic data as FAIR Semantic Data: COVID-19 as a case study,” funded by Indian Statistical Institute Kolkata. This work was conducted using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health. Thanks to Franz Inc. (www.allegrograph.com) for their help with AllegroGraph and Gruff. Thanks to Dr Sivaram Arabandi, MD, for his feedback on the CODO ontology.
Citation
DeBellis, M. and Dutta, B. (2022), "From ontology to knowledge graph with agile methods: the case of COVID-19 CODO knowledge graph", International Journal of Web Information Systems, Vol. 18 No. 5/6, pp. 432-452. https://doi.org/10.1108/IJWIS-03-2022-0047
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited