A Community-driven vision for a new Knowledge Resource for AI
Vinay K Chaudhri, Chaitan Baru, Brandon Bennett, Mehul Bhatt, Darion Cassel, Anthony G Cohn, Rina Dechter, Esra Erdem, Dave Ferrucci, Ken Forbus, Gregory Gelfond, Michael Genesereth, Andrew S. Gordon, Benjamin Grosof, Gopal Gupta, Jim Hendler, Sharat Israni, Tyler R. Josephson, Patrick Kyllonen, Yuliya Lierler, Vladimir Lifschitz, Clifton McFate, Hande K. McGinty, Leora Morgenstern, Alessandro Oltramari, Praveen Paritosh, Dan Roth, Blake Shepard, Cogan Shimzu, Denny Vrandečić, Mark Whiting, Michael Witbrock
TL;DR
The paper addresses the need for a verifiable, general-purpose knowledge resource to fill gaps in AI infrastructure left by current knowledge graphs and implicit LLM knowledge. It proposes a community-driven, open engineering framework to host modular knowledge representations, support formal reasoning, and enable cross-domain interoperability. Core contributions include a structured vision for foundational knowledge, automated reasoning, knowledge curation with human-in-the-loop and LLMs, modernized education, robust evaluation, and a concrete proof-of-concept plan with industry use cases. If realized, the resource could enhance transparency, guarantees, and reusable knowledge modules across robotics, healthcare, education, and scientific research, ultimately strengthening AI systems with grounded, context-aware reasoning.
Abstract
The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered over 50 researchers to explore these questions. This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure. In addition to leveraging contemporary advances in knowledge representation and reasoning, one promising idea is to build an open engineering framework to exploit knowledge modules effectively within the context of practical applications. Such a framework should include sets of conventions and social structures that are adopted by contributors.