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Facilitating the Analysis of COVID-19 Literature Through a Knowledge Graph

  • Conference paper
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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12507))

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Abstract

At the end of 2019, Chinese authorities alerted the World Health Organization (WHO) of the outbreak of a new strain of the coronavirus, called SARS-CoV-2, which struck humanity by an unprecedented disaster a few months later. In response to this pandemic, a publicly available dataset was released on Kaggle which contained information of over 63,000 papers. In order to facilitate the analysis of this large mass of literature, we have created a knowledge graph based on this dataset. Within this knowledge graph, all information of the original dataset is linked together, which makes it easier to search for relevant information. The knowledge graph is also enriched with additional links to appropriate, already existing external resources. In this paper, we elaborate on the different steps performed to construct such a knowledge graph from structured documents. Moreover, we discuss, on a conceptual level, several possible applications and analyses that can be built on top of this knowledge graph. As such, we aim to provide a resource that allows people to more easily build applications that give more insights into the COVID-19 pandemic.

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Notes

  1. 1.

    https://github.com/RDFLib/rdflib.

  2. 2.

    https://github.com/kingfish777/COVID19.

  3. 3.

    https://github.com/rmlio/yarrrml-parser.

  4. 4.

    https://github.com/GillesVandewiele/COVID-KG.

  5. 5.

    www.kaggle.com/iljara/covid-19-knowledge-graph-a-network-analysis.

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Author information

Authors and Affiliations

  1. IDLab, Ghent University – imec, Technologiepark-Zwijnaarde 126, Ghent, Belgium

    Bram Steenwinckel, Gilles Vandewiele, Ilja Rausch, Pieter Heyvaert, Ruben Taelman, Pieter Colpaert, Pieter Simoens, Anastasia Dimou, Filip De Turck & Femke Ongenae

Authors
  1. Bram Steenwinckel
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  2. Gilles Vandewiele
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  3. Ilja Rausch
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  4. Pieter Heyvaert
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  5. Ruben Taelman
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  6. Pieter Colpaert
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  7. Pieter Simoens
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  8. Anastasia Dimou
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  9. Filip De Turck
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  10. Femke Ongenae
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Corresponding author

Correspondence to Bram Steenwinckel .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Steenwinckel, B. et al. (2020). Facilitating the Analysis of COVID-19 Literature Through a Knowledge Graph. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-62466-8_22

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