A Community-driven vision for a new Knowledge Resource for AI
This work tackles a foundational problem in AI by aiming to create a widely available knowledge resource, but it is incremental as it builds on existing ideas like Cyc and modern knowledge representation.
The paper addresses the lack of comprehensive, verifiable knowledge resources in AI, which hinders tasks like language modeling and robotic planning, and proposes a community-driven vision for a new knowledge infrastructure based on findings from a AAAI workshop.
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.