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

Principle Investigator: Prof. Dr. Sven Groppe

Research Associate: Hanieh Khorashadizadeh

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.

Partners:

Université Paris Cité (coordinator)
Université de Toulouse

Activities:

Keynote Speaker: Sven Groppe, Leveraging Artificial Intelligence and Machine Learning in Pandemics using COVID-19 as a Case Study, Innovative Science and Technology after the Emergence of COVID-19: The 25th SANKEN International Symposium (online), Osaka, Japan
Keynote Speaker: Sven Groppe, Leveraging Artificial Intelligence and Machine Learning in Pandemics using COVID-19 as a Case Study, International Semantic Intelligence Conference (ISIC 2022) / International Healthcare Informatics Conference (IHIC 2022) (online)
Tutorials: Sven Groppe, Sanju Tiwari, Farah Benamara, Soror Sahri, Analysis of the Impact of COVID-19 Ontologies, The Knowledge Graph Conference (KGC), New York (online), USA
Invided Speaker: Dr. Sanju Tiwari DAAD Postdoc-Net-AI-Fellow, From Ontologies to Knowledge Graphs An Overview

Publications

2023

Hanieh Khorashadizadeh, Frédéric Ieng, Morteza Ezzabady, Soror Sahri, Sven Groppe, Farah Benamara: Evaluation approaches of personal knowledge graphs
in: Personal Knowledge Graphs (PKGs): Methodology, tools and applications, p.277-293, Institution of Engineering and Technology, 2023
Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari, Jinghua Groppe, Sven Groppe: Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
in: CoRR, 2023, Vol.abs/2305.08804
Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari, Jinghua Groppe, Sven Groppe: Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
in: Proceedings of the 2nd International Workshop on Knowledge Graph Generation From Text (Text2KG) in conjunction with the Extended Semantic Web Conference (ESWC 2023), Hersonissos, Greece, 2023

nach oben

2022

Sven Groppe, Sanju Tiwari, Hanieh Khorashadizadeh, Jinghua Groppe, Tobias Groth, Farah Benamara, Soror Sahri: Short Analysis of the Impact of COVID-19 Ontologies
in: International Semantic Intelligence Conference (ISIC 2022), online, 2022
Hanieh Khorashadizadeh, Sanju Tiwari, Sven Groppe: A Survey on Covid-19 Knowledge Graphs and their Data Sources
in: Proceedings of the EAI International Conference on Intelligent Systems and Machine Learning (EAI ICISML 2022), 2022