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.
View on arXiv@article{chaudhri2025_2506.16596,
title={ A Community-driven vision for a new Knowledge Resource for AI },
author={ Vinay K Chaudhri and Chaitan Baru and Brandon Bennett and Mehul Bhatt and Darion Cassel and Anthony G Cohn and Rina Dechter and Esra Erdem and Dave Ferrucci and Ken Forbus and Gregory Gelfond and Michael Genesereth and Andrew S. Gordon and Benjamin Grosof and Gopal Gupta and Jim Hendler and Sharat Israni and Tyler R. Josephson and Patrick Kyllonen and Yuliya Lierler and Vladimir Lifschitz and Clifton McFate and Hande K. McGinty and Leora Morgenstern and Alessandro Oltramari and Praveen Paritosh and Dan Roth and Blake Shepard and Cogan Shimzu and Denny Vrandečić and Mark Whiting and Michael Witbrock },
journal={arXiv preprint arXiv:2506.16596},
year={ 2025 }
}