Towards Integrated and Open COVID-19 Data

TL;DR

This paper presents an ontology-based system for integrating and analyzing COVID-19 data from multiple European countries, enabling easier data exploration and knowledge discovery through a publicly accessible RDF knowledge base.

Contribution

It introduces an approach for data acquisition, transformation to RDF, and interlinking of COVID-19 data from various countries, facilitating joint analysis and insights.

Findings

Successfully integrated data from seven European countries.

Automated updating of the knowledge base.

Enabled complex spatio-temporal queries for analysis.

Abstract

Motivated by the global unrest related to the COVID-19 pandemic, we present a system prototype for ontology-based, integration of national data published from various countries. COVID-related data is published from different authorities, in different formats, at varying spatio-temporal granularity, and irregularly. Consequently, this hinders the joint data exploration and exploitation, which could lead scientists to acquire important insights, without having to deal with the cumbersome task of data acquisition and integration. Motivated by this shortcoming, we propose an approach for data acquisition, ontology-based data representation, and data transformation to RDF, which also enables interlinking with other publicly available data sources. Currently, data coming from the following European countries has been successfully integrated: Austria, Belgium, France, Germany, Greece, Italy, and Sweden. The knowledge base is automatically being updated, and it is available to the public through a SPARQL endpoint and a direct download link. Furthermore, we showcase how data integration enables spatio-temporal data analysis and knowledge discovery, by means of meaningful queries that would not be feasible to process otherwise.