Wikidata And Covid-19: Creating A Collaborative Knowledge Graph From Cord-19 Scholarly Publications
dc.contributor.author | Turki, Houcemeddine | |
dc.date.accessioned | 2024-03-18T09:46:50Z | |
dc.date.available | 2024-03-18T09:46:50Z | |
dc.date.issued | 2020-09-24 | |
dc.description.abstract | Knowledge 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.doi | https://doi.org/10.5281/zenodo.4051192 | |
dc.identifier.uri | https://africarxiv.ubuntunet.net/handle/1/809 | |
dc.identifier.uri | https://doi.org/10.60763/africarxiv/762 | |
dc.identifier.uri | https://doi.org/10.60763/africarxiv/762 | |
dc.identifier.uri | https://doi.org/10.60763/africarxiv/762 | |
dc.subject | Public health surveillance | |
dc.subject | Wikidata | |
dc.subject | Knowledge graph construction | |
dc.title | Wikidata And Covid-19: Creating A Collaborative Knowledge Graph From Cord-19 Scholarly Publications |