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.
Highlights
PR The COVID-19 pandemic is complex and multifaceted and touches on almost every aspect of current life [25]
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
Coordinating efforts to systematize and formalize knowledge about COVID-19 in a computable form is key in accelerating our response to the pathogen and future epidemics [24]
Summary
PR The COVID-19 pandemic is complex and multifaceted and touches on almost every aspect of current life [25]. There are already attempts at creating community-based ontologies of COVID-19 knowledge and data [37], as well as efforts to aggregate expert data. The interconnected, multidisciplinary, and international nature of the pandemic creates both challenges and opportunities for using knowledge graphs. For applications of knowledge graphs in general, common challenges include the timely assessment of the rel-. E lated to leveraging such knowledge graphs for real-life applications, which in the case of COVID-19 could be, for instance, outbreak management in a specific societal context or education about the virus or about countermea-. Integrating COVID-19 data presents particular challenges: First, human knowledge about the COVID-19 disease, the underlying pathogen and the resulting pandemic is evolving rapidly [53], so systems representing it need to be flexible and scalable in terms of their data models and workflows, yet quick in terms of deployability and updatability. Despite the disruptions that the pandemic has brought to many communities and infrastructures [25], the curated data about it should ideally be and reliably accessible for humans and machines across a broad range of use cases [82]
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