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

  • Bram SteenwinckelEmail author
  • Gilles Vandewiele
  • Ilja Rausch
  • Pieter Heyvaert
  • Ruben Taelman
  • Pieter Colpaert
  • Pieter Simoens
  • Anastasia Dimou
  • Filip De Turck
  • Femke Ongenae
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12507)


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.


COVID-19 Knowledge graph creation Network analysis Graph embeddings 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IDLabGhent University – imecGhentBelgium

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