I-Corps: A Trustworthy, Interactive, Up to Date COVID-19 Knowledge Graph

The broader impact/commercial potential of this I-Corps project is the development of an interactive, easy to use knowledge graph populated with trustworthy information from the latest published medical findings on COVID-19. Easy access to the vetted medical findings may motivate people to make informed decisions, which is expected to lead to better health practices. The solution may save many lives in the US and worldwide. Additionally, this knowledge graph can extend to other diseases and create accessible, easy to use, trustworthy medical practices for aging, cancer, cardiovascular diseases, diabetes, etc. This I-Corps project is based on the development of an interactive Knowledge Graph (KG) populated with trustworthy information from the latest published medical findings on COVID-19. Currently existing, socially maintained KGs lack COVID-19 medical findings and scalable mechanisms to keep the graphs up to date. The proposed solution includes the design and evaluation of new scalable algorithms and abstractions. The technology incorporates COVID symptoms and possible vaccine side-effects in non-relational tables having different structures and metadata. The team has constructed the initial "skeleton" of the graph and proposes to automatically process tables from recent publications in order to enrich the KG. While most medical tables are complex, exhibiting hierarchical vertical/horizontal metadata, this technology addresses the fundamental challenges by providing a novel, scalable hybrid graph, incorporating new abstractions to handle complex tabular data, developing a multi-layer deep-/machine-Learning network with new 2D tabular embedding layer, and designing a new search engine for graph and medical tables. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.