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Iberoamerican Knowledge Graphs and Semantic Web Conference

KGSWC 2021: Knowledge Graphs and Semantic Web pp 153–168Cite as

The Covid-19 CODO Development Process: an Agile Approach to Knowledge Graph Development

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1459))

Abstract

The CODO ontology was designed to capture data about the Covid-19 pandemic. The goal of the ontology was to collect epidemiological data about the pandemic so that medical professionals could perform contact tracing and answer questions about infection paths based on information about relations between patients, geography, time, etc. We took information from various spreadsheets and integrated it into one consistent knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph. The ontology is published on Bioportal and has been used by two projects to date. This paper describes the process used to design the initial ontology and to develop transformations to incorporate data from the Indian government about the pandemic. We went from an ontology to a large knowledge graph with approximately 5M triples in a few months. Our experience demonstrates some common principles that apply to the process of scaling up from an ontology model to a knowledge graph with real-world data.

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Notes

  1. 1.

    http://www.allegrograph.com.

  2. 2.

    https://bioportal.bioontology.org/ontologies/IDO-COVID-19.

  3. 3.

    https://bioportal.bioontology.org/ontologies/COVID19.

  4. 4.

    https://www.ebi.ac.uk/ols/ontologies/covoc.

  5. 5.

    We utilized Lisp because our team was very small (2 people), and the lead developer had the most experience in Lisp and we wanted to work as rapidly as possible to meet the needs of the pandemic. In future versions we will re-implement the functions in Python.

  6. 6.

    The details of the CODO project can be found at: https://github.com/biswanathdutta/CODO. The CODO ontology can be found at: https://bioportal.bioontology.org/ontologies/CODO.

  7. 7.

    See [17] for the actual Lisp and SPARQL code for this and all transformations.

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Acknowledgement

This work has been supported 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. (http://www.allegrograph.com) for their help with AllegroGraph and Gruff. Thanks to Dr. Sivaram Arabandi, MD for his feedback on the CODO ontology.

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Authors and Affiliations

  1. San Francisco, CA, USA

    Michael DeBellis

  2. Indian Statistical Institute, Bangalore, India

    Biswanath Dutta

Authors
  1. Michael DeBellis
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  2. Biswanath Dutta
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Correspondence to Michael DeBellis .

Editor information

Editors and Affiliations

  1. Tinamica/Universidad Internacional de la Rioja, Madrid, Madrid, Spain

    Boris Villazón-Terrazas

  2. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. Texas A&M University – Kingsville, Kingsville, TX, USA

    Ayush Goyal

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    MA Jabbar

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DeBellis, M., Dutta, B. (2021). The Covid-19 CODO Development Process: an Agile Approach to Knowledge Graph Development. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-91305-2_12

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  • Online ISBN: 978-3-030-91305-2

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