Using Historical Knowledge Graphs To Materialize And Visualize Corona Viruses Knowledge Evolution

Knowledge Graphs (KG) or, more generally speaking, Knowledge bases implemented using Semantic Web technologies provide the basics to represent domain knowledge in a machine interpretable manner. This allows computers to consume this knowledge for various purposes (e.g. information retrieval, knowledge discovery, decision support...). Because of their properties, KG are currently used to formalize knowledge about Covid 19 to provide a deep understanding of the state of knowledge about the disease at a given moment in time (e.g. CORD 19, Lens Covid 19 datasets, John Hopkins university datasets). However, only the most recent state of knowledge is retained in these graphs and the history of the disease (i.e. how our knowledge about the disease has evolved) is rarely preserved. In consequence, crucial information about the disease is lost. As a direct consequence, past knowledge described with outdated terminology/concepts can hardly be retrieved which is the case of scientific literature indexed with an outdated ontology. This phenomenon also impact patient data that cannot be retrieved because annotated with an older version of the knowledge graph and, not identified at recruitment time for clinical trials. In this project we will construct an historical knowledge graph about the Covid 19 disease based on existing knowledge graph (cf COVlit project at LCSB) and corpora of scientific articles.