The concept of an Open Knowledge Network (OKN) is one of the components of the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea, with the objective of providing semantic information infrastructure. By encoding information and knowledge about real-world entities and their relationships, the OKN would enable next generation artificial intelligence-based technologies and applications, focusing in particular on science and engineering information. While large-scale knowledge networks have been deployed in services like Google Search, Amazon catalogs, Apple Siri, Microsoft Cortana, and WolframAlpha, an open effort would expand this approach, enabling discovery of non-trivial information from multiple disparate knowledge sources for thousands of new topic areas in scientific and engineering information.
With this objective in mind, the NSF Convergence Accelerator announced a “track” on the Open Knowledge Network (Track A) in 2019. Twenty-one projects were funded in Phase I in 2019 and five were funded in Phase II in 2020. Multidisciplinary, multi-sector teams in these projects are addressing a range of issues including, programming environments for knowledge network creation, making hidden/implied geospatial information explicit in knowledge graphs, and encoding information in specific domains, viz. urban flooding, biomedicine, and court records.
This talk will provide an overview of the NSF Convergence Accelerator program and the Open Knowledge Network activities.