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.
Houcemeddine Turki
Medical student
My research interests include the development of a large-scale framework for using open resources and semantic technologies for driving biomedical informatics and research evaluation at a low cost.
Mohamed Ali Hadj Taieb
Assistant professor
My research interests include semantic similarity, semantic relatedness, knowledge representation, Big Data, social media, data management systems and graph embedding.
Mohamed Ben Aouicha
Associate professor
My research interests concern information retrieval, semantic technologies, social media analytics, knowledge representation, Big Data and graph embedding.