A Community-driven vision for a new Knowledge Resource for AI

TL;DR

This paper advocates for a community-driven, open framework to develop a comprehensive, versatile knowledge resource for AI, integrating modern representation, reasoning, and collaborative standards to address current knowledge gaps.

Contribution

It proposes a new community-based approach to creating a versatile AI knowledge resource, emphasizing open engineering frameworks and social conventions for collaboration.

Findings

Identified critical knowledge gaps in AI systems.

Highlighted the success and limitations of existing knowledge resources.

Suggested a community-driven, open framework for future development.

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.