High Quality Knowledge Graphs from recent English, French and German Emergent Trends with the example of COVID-19

• Applicant: Professor Dr. Sven Groppe
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 490998901

The COVID-19 pandemic has stopped social and economical activities today. The total cost of the recent pandemic is estimated by 16 trillion USD by only considering the US and aggregating mortality, morbidity, mental health conditions, and direct economic losses on the assumption of the pandemic is substantially ending in fall of 2021. Hence an extensive analysis of the COVID-19 outbreak and the global responses are essential for preparing humanity for such future situations. Since the early 2020, hundreds of studies have been carried out to analyse, understand, track and model various aspects of the pandemic. Our project aims at providing the means for such kind of analysis, focusing for the first time at capturing inconsistencies/complementarities between these studies through (1) a general view of how facts about the pandemic evolve across time and languages, and (2) a high quality evaluation of these facts in enriched knowledge graphs to support further analysis. This is a highly collaborative project involving complementary expertise from natural language processing, databases and knowledge graph in order to generate high-quality knowledge graphs for emergent English, French and German trends with the example of COVID-19. The methodology and results of QualityOnt were designed to be generic enough to ensure their reusability in other future sanitary crises situations.

• DFG Programme: Research Grants
• International Connection: France
• Cooperation Partners: Professorin Farah Benamara; Professorin Dr. Soror Sahri