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Wikidata And Covid-19: Creating A Collaborative Knowledge Graph From Cord-19 Scholarly Publications

dc.contributor.authorTurki, Houcemeddine
dc.date.accessioned2024-03-18T09:46:50Z
dc.date.available2024-03-18T09:46:50Z
dc.date.issued2020-09-24
dc.description.abstractKnowledge graphs are an essential ingredient for information systems to handle the ever growing COVID-19 data on a daily basis. This presentation explains how open and collaborative FAIR knowledge bases like Wikidata can be useful to create a large-scale semantic representation of COVID-19 information from CORD-19 scholarly publications. I give an overview of how a data model has been collaboratively developed and maintained for COVID-19 knowledge, and I provide a detailed snapshot about the various methods used to extract items and statements from CORD-19 research papers. Then, I outline the tools for the enrichment of COVID-19 information on Wikidata as well as the knowledge graph validation methods applicable to COVID-19 knowledge. Finally, I describe the COVID-19 information in Wikidata and discuss its usefulness in supporting human decisions and social recommendations about the infectious disease.
dc.identifier.doihttps://doi.org/10.5281/zenodo.4051192
dc.identifier.urihttps://africarxiv.ubuntunet.net/handle/1/809
dc.identifier.urihttps://doi.org/10.60763/africarxiv/762
dc.identifier.urihttps://doi.org/10.60763/africarxiv/762
dc.identifier.urihttps://doi.org/10.60763/africarxiv/762
dc.subjectPublic health surveillance
dc.subjectWikidata
dc.subjectKnowledge graph construction
dc.titleWikidata And Covid-19: Creating A Collaborative Knowledge Graph From Cord-19 Scholarly Publications

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