Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata
Semantic Web
; 13(2):233-264, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1674286
ABSTRACT
Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
Computers; Public health surveillance; Wikidata; knowledge graph; COVID-19; SPARQL; community curation; FAIR data; linked open data; Decision analysis; Collaboration; Knowledge bases (artificial intelligence); Cooperative work; Databases; Information retrieval; Pandemics; Open data; Multilingualism; Resource Description Framework-RDF; Graphical representations; Coronaviruses; Knowledge representation; Query languages
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Semantic Web
Year:
2022
Document Type:
Article
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