CODO: An Ontology for Collection and Analysis of Covid-19 Data

TL;DR

CODO is an open-source ontology designed to standardize and integrate diverse COVID-19 data sources for better analysis and understanding of the pandemic.

Contribution

It introduces a standards-based, reusable ontology for COVID-19 data collection and analysis, incorporating real-world data and best practices in semantic modeling.

Findings

Successfully integrated data from Indian government sources.

Supports heterogeneous data sources through standards-based modeling.

Has an active user community and real-world application.

Abstract

The COviD-19 Ontology for cases and patient information (CODO) provides a model for the collection and analysis of data about the COVID-19 pandemic. The ontology provides a standards-based open-source model that facilitates the integration of data from heterogeneous data sources. The ontology was designed by analysing disparate COVID-19 data sources such as datasets, literature, services, etc. The ontology follows the best practices for vocabularies by re-using concepts from other leading vocabularies and by using the W3C standards RDF, OWL, SWRL, and SPARQL. The ontology already has one independent user and has incorporated real-world data from the government of India.