COVID*Graph provides researchers with the latest scientific data in the Knowledge Graph
Munich, April 3, 2020 - Scientists, developers and data scientists are becoming increasingly involved in the fight against the COVID-19 pandemic. The COVID*Graph project is actively developing a Knowledge Graph to provide researchers with free and easy access to the latest research data. The aim is to gain important insights into the spread and course of the coronavirus as quickly as possible, and thus come one step closer to developing a vaccine.
The not-for-profit project was launched in early March with the support of the German Center for Diabetes Research (DZD), Kaiser&Preusse, PRODYNA, Structr and yWorks, among others. The COVID*Graph team uses the graph database Neo4j to bundle scientific publications and research work in a central COVID-19 knowledge hub. Publicly available data sources on the coronavirus are linked with current and existing relevant patent specifications as well as data sets from genome and molecular biology databases. Currently, the Knowledge Graph comprises more than 16 million nodes and over 65 million edges, with the database growing each day.
"In recent months, a great deal has been published very quickly about the coronavirus. The COVID-19 Open Research Database (CORD-19) alone stands at 44,000 scientific articles. It is difficult for medical research to keep an overview - especially since there hasn't been time to validate the work in the usual way," explains Dr. Alexander Jarasch, Head of Bioinformatics and Data Management at the German Center for Diabetes Research and co-initiator of COVID*Graph. "With our project we want to help researchers and scientists to find a quick and uncomplicated way through the vast amount of information. Therefore we are also happy about every form of support and cooperation".
Knowledge graphs are semantic knowledge databases in which a large amount of heterogeneous data from different sources can be stored, linked and queried. The intuitive model consisting of nodes and edges makes it possible to illuminate collected knowledge clearly, to uncover connections and to recognize patterns. "The COVID*Graph provides the data basis for understanding the processes involved in a coronavirus infection. Why is this virus so contagious? And why do such severe complications occur? Linking large data sets and evaluating them provides new insights and provides researchers with approaches and hypotheses for their further research work," explains Dr. Martin Preusse, founder of Kaiser&Preusse, who co-initiated the COVID*Graph project.
Invitation to the Neo4j initiative "Graphs4Good"
Graph databases are widely used in data analysis - in medical research and drug development as well as in supply chain management and logistics. In the fight against COVID-19, graph analytics can be used, for example, to detect contacts of infected people (clusters). Shortest path algorithms can also be used to trace infection paths across multiple contact points, and to determine optimal supply chains and transport routes. The number of graph-based projects in the graph community has increased significantly since the outbreak of the pandemic. In addition to scientific projects such as COVID*Graph, these include smaller initiatives that, for example, help risk groups at a local level or help businesses and companies.
For this reason, Neo4j has included all COVID-19 relevant graph projects in its "Graphs4Good" program. Users who use graph technology in the fight against the corona virus will receive free access to the enterprise version of the Neo4j database on request. In addition, Neo4j offers help in finding mentors, sharing datasets and exchanging information within the community. Data scientists, developers, researchers, graph enthusiasts and tech-interested people are also invited to participate in the virtual Graphs4Good Hackathon.