The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.
The goal of this project is to build infrastructure for efficient construction of knowledge networks and applications, as well as to demonstrate the system with concrete knowledge networks that describe COVID-19 science and economics. In the short term, this work will lead to high accuracy data resources that will be useful to scientists and policy makers in addressing the virus and its economic impact. Sample goals include enabling a medical researcher to quickly identify relevant candidate drugs, and a policy maker to quickly evaluate the likely impacts of a novel law. The project will create programming tools that will make knowledge networks and their applications far less expensive to build. This infrastructure of programming tools will facilitate the creation of a large and novel set of informational tools and will also significantly expand the set of people who can participate in creating knowledge network resources. Because knowledge networks combine unique data analysis qualities with the topical breadth of the entire World Wide Web, the potential growth of knowledge tools is very large and potentially transformative.
This project includes partnerships with a strong set of non-academic and academic partners. This convergence research team will integrate their multidisciplinary expertise in data management, artificial intelligence, programming languages, biomedical topics relevant to COVID-19, and economics, with the other domains represented in the projects funded in the Track A Phase II cohort.
Creating this knowledge programming infrastructure and concrete knowledge networks will require solving several technical challenges. The first is an intelligent “knowledge compilation layer†that makes useful but rapidly-changing knowledge networks appear to be stable enough for programmers to use them when writing reliable code. The second is the creation of a mechanism for transparently sharing knowledge resources and debugging information within and across organizations. The third is a method for collecting knowledge provenance metadata — details about how every individual data element was created — via automatic instrumentation of user software. A last challenge is the creation of knowledge-from-document systems that can produce high accuracy knowledge networks with very little explicit human oversight.
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.
