A community‐driven vision for a new knowledge resource for AI

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.

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Publication Details

Journal
AI Magazine
Published
2025-10-26
DOI
https://doi.org/10.1002/aaai.70035
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

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article

A community‐driven vision for a new knowledge resource for AI

Brandon Bennett, Leora Morgenstern, Chaitan Baru, Yuliya Lierler et al.
AI Magazine
Semantic Web and Ontologies
article

A community‐driven vision for a new knowledge resource for AI

Brandon Bennett, Leora Morgenstern, Chaitan Baru, Yuliya Lierler, James Hendler, Michael Genesereth, Esra Erdem, Denny Vrandečić, Vinay K. Chaudhri, Dave Ferrucci, Dan Roth, Anthony G. Cohn, Benjamin N. Grosof, Gregory Gelfond, Blake Shepard, Alessandro Oltramari, Rina Dechter, Michael Witbrock, Sharat Israni, Mehul Bhatt, Gopal Gupta, Mark E. Whiting, Tyler R. Josephson, Praveen Paritosh, Vladimir Lifschitz, Patrick C. Kyllonen, Clifton McFate, Ken Forbus, Andrew S. Gordon, D. G. Cassel, Hande Küçük McGinty, Cogan Shimizu
article en

Abstract

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.

AI MagazineVol. 46(4)
Northwestern University (US), University of Nebraska at Omaha (US), Turing Institute (GB), University of Dayton (US), Defense Advanced Research Projects Agency (US), SRI International (US), American Enterprise Institute (US), University of Leeds (GB), U.S. National Science Foundation (US), Amazon (United States) (US), NorthShore University HealthSystem (US), Educational Testing Service (US), Sabancı Üniversitesi (TR), Oracle (United States) (US), University of Auckland (NZ), The University of Texas at Dallas (US), Rensselaer Polytechnic Institute (US), University of California, San Francisco (US), University of Dallas (US), Kansas State University (US), Wright State University (US), University of California, Irvine (US), Örebro University (SE), Third Way (US), Creative Technologies (United States) (US), Robert Bosch (United States) (US), Wikimedia Foundation (US), The Alan Turing Institute (GB), Irvine University (US), Knowledge Based Systems (United States) (US), Cycorp (United States) (US), Carnegie Mellon University (US), University of Maryland, Baltimore County (US), University of Pennsylvania (US), The University of Texas at Austin (US), University of North Texas at Dallas (US), Philadelphia University (US), Cytel (United States) (US), Stanford University (US)
National Science Foundation, California Institute of Technology, Alan Turing Institute, University of Cambridge, Linköpings Universitet, Tongji University, High Energy Physics
Openalex Percentile: Top 84%
Semantic Web and Ontologies
DOIOpen Access PDFOpenAlex
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