Three critical revolutions are shaping the landscape of AI: deep learning, knowledge graphs and automated reasoning. While deep learning has unleashed powerful tools such as ChatGPT, knowledge graphs and automated reasoning are providing essential backbone services for major coporations through applications such as business intelligence and automated verification. There is currently no resource pooling for knowledge graphs and automated reasoning. The goal of this workshop is to catalyze a global effort to fill this gap by creating a Translational Institute for Knowledge Axiomatization (TIKA). TIKA is envisioned to serve as a hub for research, education, training, and  hosting of repositories of open-source knowledge based on open data sources. The institute would focus on "use-inspired" research, addressing pragmatic issues in translating advances in knowledge graphs and reasoning to practice.

TIKA is envisioned to consider knowledge in all forms including structured data, rules, controlled languages, mathematical formulas, etc. While AI for science must incorporate domain intelligence, it should also include knowledge to enable common sense reasoning and deep inferential reasoning that commercial knowledge graphs do not do today. The objectives of TIKA would be to pursue more human like learning that would be much more efficient and would not require as much training data and effort as the current generation of deep learning. This form of learning has also been referred to as "learning by being told" in the sense of McCarthy's advice taker. TIKA is to advance the state-of-the-art in knowledge axiomatization by taking into account experience  from prior efforts such as Cyc, Project Halo, Wolfram Alpha, ProtoOKN and the Linguistic Data Consortium.

The idea for a “Knowledge Axiomatization Institute” was proposed a number of years ago by Late Prof. Doug Lenat—a pioneer in artificial intelligence and creator of the Cyc system. The initiative for a Translational Institute for Knowledge Axiomatization pays homage to Prof. Lenat’s pioneering vision and ideas in this area. Along with his conviction about systems for knowledge representation and “common sense” reasoning, he was firmly committed to the idea of teaching knowledge representation at every level from high school to computer science undergraduates, and above.