Abstract: Open access to shared information is essential for the development and evolution of artificial intelligence (AI) and AI-powered solutions needed to address the complex challenges facing the nation and the world. Data and information should be easy to find, access, and reuse. This talk will describe the open knowledge network (OKN)—an effort to develop an open information infrastructure based on an interconnected network of knowledge graphs that would serve as essential public-data infrastructure to facilitate integration of diverse information needed to develop solutions to a wide range of societal issues, from economic development to climate change to social equity.
Knowledge graphs are an important type of knowledge structure to enable data integration. They consist of nodes and edges — where nodes represent real-world entities (e.g., a city, a neighborhood, a court case, a gene, a chemical compound), and edges represent different types of relationships among nodes.
In February 2022, the National Science Foundation in partnership with the White House Office of Science and Technology Policy, launched an OKN Innovation Sprint, which harnessed the collective insights of roughly 150 experts from government, industry, academia, and nonprofit organizations to help build a roadmap for a Prototype OKN (Proto-OKN), based on specific use cases and various end-user perspectives. The findings from this Sprint are summarized in the OKN Roadmap Report. Creation of OKN is fundamentally a sociotechnical effort, that must consider human, social and organizational factors, than merely a technical effort. Deep engagement is necessary among domain knowledge experts and a host of other stakeholders including data owners, decision-makers, various end-user communities, tool builders, and knowledge representation experts.
The goals and objectives of OKN are in complete alignment with the objectives expressed in the Nelson Memo released in August 2022, which seeks to ensure free, immediate, and equitable access to federally funded research results and data. As stated in the memo, “When federally funded research is available to the public, it can improve lives, provide policymakers with important evidence with which to make critical decisions, accelerate the rates of discovery and translation, and drive more equitable outcomes across every sector of society.” The OKN would be a key component in providing access to such trustworthy information.
Speaker: Dr. Chaitanya Baru joined as Senior Advisor in NSF’s new Technology, Innovation, and Partnerships (TIP) Directorate in October 2022 after a 25-year career at the San Diego Supercomputer Center (SDSC), University of California, San Diego. During that time he also served on assignment at NSF–first as Senior Advisor for Data Science in the Computer and Information Science and Engineering (CISE) Directorate from 2014-2018, and then as Senior Advisor for the NSF Convergence Accelerator, from 2019-2021. While Senior Advisor for Data Science, he co-chaired the NSF Harnessing the Data Revolution Big Idea (HDR) and played a leadership role in the NSF BIGDATA program. He helped initiate the HDR Data Science Corps program, helped design the NSF Big Data Regional Innovations Hubs and TRIPODS programs, and supported a series of workshops on Translational Data Science. He was a lead organizer of the inter-agency Workshop on Open Knowledge Network, which led to establishment of Track A in the Convergence Accelerator on Open Knowledge Network (OKN). At SDSC, Dr. Baru’s work focused on R&D in data and knowledge systems, especially centered around translational and applied research issues in computer science and data science. His research collaborations have spanned a wide range of disciplines from neuroscience and behavioral medicine to geoscience, ecological science, anthropology, and others. Prior to joining SDSC, he was with the database R&D group at IBM and, before that, a member of the faculty in the EECS Department, University of Michigan, Ann Arbor. Baru has an ME and PhD in Electrical Engineering (Computer Engineering) from the University of Florida and a BTech in Electronics Engineering from the Indian Institute of Technology, Madras.