Eager: feasibility study for a translational institute on knowledge axiomatization (TIKA)

This $300,000 EAGER Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program will support a feasibility study for establishing the Translational Institute on Knowledge Axiomatization (TIKA). The primary goals are to improve artificial intelligence (AI) systems' ability to organize and apply knowledge effectively beyond deep learning techniques.

Key activities include developing educational workshops and coursework on knowledge graph construction, establishing a sustainable operational framework for maintaining TIKA's knowledge resources, and creating open educational tools and training materials. This will help expand AI research, broaden learning opportunities, and lay the groundwork for long-term advancements in AI knowledge systems. A $21,000 sub-award to Knowledge Systems Research LLC will support leading the community-building workshops and designing the knowledge axiomatization course content.

Abstract

Artificial intelligence (AI) has made major advances through deep learning, powering technologies like chatgpt and self-driving cars. However, deep learning systems often struggle to organize and apply knowledge effectively. This eager project will explore the possibility of creating the translational institute on knowledge axiomatization (TIKA) to address this challenge. TIKA would help AI better understand and use information by structuring IT into networks, making logical connections, and improving decision-making. The institute would offer education and training programs, build a central collection of knowledge resources, and support new research and industry partnerships. These efforts will expand AI beyond deep learning, leading to smarter, more adaptable systems. This eager award will help advance AI research, create new learning opportunities, and strengthen the U.S. Science and technology workforce. This eager will take three key steps to help AI better organize and use knowledge through knowledge axiomatization. First, IT will develop community-building workshops and expand the existing prototype open knowledge network (PROTO-OKN) initiatives to create and distribute coursework on knowledge graphs. Second, IT will establish a sustainable operational framework to maintain TIKA?S knowledge resources beyond the initial funding period. Third, the project will build open educational resources and training materials, equipping researchers and practitioners with tools to construct and apply knowledge graphs effectively. The project?S outcomes will enhance AI research, broaden educational opportunities, and establish a foundation for long-term advancements in AI knowledge systems, ensuring both scientific and societal impact. 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.- subawards are planned for this award.