Advanced search
2 files | 4.46 MB Add to list

Facilitating the analysis of COVID-19 literature through a knowledge graph

Bram Steenwinckel (UGent) , Gilles Vandewiele (UGent) , Ilja Rausch (UGent) , Pieter Heyvaert (UGent) , Ruben Taelman (UGent) , Pieter Colpaert (UGent) , Pieter Simoens (UGent) , Anastasia Dimou (UGent) , Filip De Turck (UGent) and Femke Ongenae (UGent)
Author
Organization
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.

Downloads

  • 7806 i.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.90 MB
  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 2.56 MB

Citation

Please use this url to cite or link to this publication:

MLA
Steenwinckel, Bram, et al. “Facilitating the Analysis of COVID-19 Literature through a Knowledge Graph.” The Semantic Web : ISWC 2020, vol. 12507, Springer, 2020, pp. 344–57, doi:10.1007/978-3-030-62466-8_22.
APA
Steenwinckel, B., Vandewiele, G., Rausch, I., Heyvaert, P., Taelman, R., Colpaert, P., … Ongenae, F. (2020). Facilitating the analysis of COVID-19 literature through a knowledge graph. In The semantic web : ISWC 2020 (Vol. 12507, pp. 344–357). Cham: Springer. https://doi.org/10.1007/978-3-030-62466-8_22
Chicago author-date
Steenwinckel, Bram, Gilles Vandewiele, Ilja Rausch, Pieter Heyvaert, Ruben Taelman, Pieter Colpaert, Pieter Simoens, Anastasia Dimou, Filip De Turck, and Femke Ongenae. 2020. “Facilitating the Analysis of COVID-19 Literature through a Knowledge Graph.” In The Semantic Web : ISWC 2020, 12507:344–57. Cham: Springer. https://doi.org/10.1007/978-3-030-62466-8_22.
Chicago author-date (all authors)
Steenwinckel, Bram, Gilles Vandewiele, Ilja Rausch, Pieter Heyvaert, Ruben Taelman, Pieter Colpaert, Pieter Simoens, Anastasia Dimou, Filip De Turck, and Femke Ongenae. 2020. “Facilitating the Analysis of COVID-19 Literature through a Knowledge Graph.” In The Semantic Web : ISWC 2020, 12507:344–357. Cham: Springer. doi:10.1007/978-3-030-62466-8_22.
Vancouver
1.
Steenwinckel B, Vandewiele G, Rausch I, Heyvaert P, Taelman R, Colpaert P, et al. Facilitating the analysis of COVID-19 literature through a knowledge graph. In: The semantic web : ISWC 2020. Cham: Springer; 2020. p. 344–57.
IEEE
[1]
B. Steenwinckel et al., “Facilitating the analysis of COVID-19 literature through a knowledge graph,” in The semantic web : ISWC 2020, planned in Athens, Greece, changed to a virtual format, 2020, vol. 12507, pp. 344–357.
@inproceedings{8680092,
  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.}},
  author       = {{Steenwinckel, Bram and Vandewiele, Gilles and Rausch, Ilja and Heyvaert, Pieter and Taelman, Ruben and Colpaert, Pieter and Simoens, Pieter and Dimou, Anastasia and De Turck, Filip and Ongenae, Femke}},
  booktitle    = {{The semantic web : ISWC 2020}},
  isbn         = {{9783030624651}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  location     = {{planned in Athens, Greece, changed to a virtual format}},
  pages        = {{344--357}},
  publisher    = {{Springer}},
  title        = {{Facilitating the analysis of COVID-19 literature through a knowledge graph}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-62466-8_22}},
  volume       = {{12507}},
  year         = {{2020}},
}

Altmetric
View in Altmetric