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

2025-06-19

AI Summary

Contemporary AI systems face a common bottleneck—deficient universal knowledge resources: large language models exhibit knowledge gaps, robots lack commonsense world knowledge, and fact-checking remains heavily reliant on manual effort. Method: This paper proposes a novel paradigm for constructing open knowledge resources as AI infrastructure, introducing the first community-driven knowledge engineering framework. It integrates standardized knowledge module interfaces, logic-enhanced graph representations, modular reasoning architectures, and socio-technical coordination protocols. Contribution/Results: The project establishes the first cross-institutional consensus roadmap for knowledge resource development, explicitly defining technical pathways, verifiable evaluation criteria, and decentralized governance mechanisms. The resulting framework provides a systematic, top-down design and empirical foundation for next-generation universal knowledge infrastructure—ensuring reusability, evolvability, and formal verifiability.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Engineering
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addressing the lack of verifiable general-purpose knowledge sources in AI
• Identifying needed knowledge resources for AI applications and evaluation
• Proposing a community-driven open framework for effective knowledge integration

Innovation

• Community-driven knowledge infrastructure development
• Open engineering framework for knowledge modules
• Modern knowledge representation and reasoning advances