构建开放协作的知识基础设施，解决AI知识缺失问题
A Community-driven vision for a new Knowledge Resource for AI
- 以社区共建方式打造可扩展的开放知识框架
- 提出需融合现代知识表示与实际应用的模块化设计
- 适合关注AI知识系统建设的研究者与开发者
长期以来，构建全面、多用途的知识资源（类似1984年Cyc项目）仍是人工智能的重要目标。尽管已有WordNet、ConceptNet、Wolfram|Alpha等知识资源取得成功，但可验证、通用且广泛可用的知识源在AI基础设施中仍严重缺乏。大语言模型存在知识盲区，机器人规划缺少世界知识，事实性错误检测高度依赖人工。当前亟需什么样的知识资源？现代技术如何推动其发展与评估？一场由超过50位研究人员参与的AAAI研讨会探讨了这些问题。本文综合成果，提出面向未来的社区驱动型知识基础设施愿景。除利用现代知识表示与推理技术外，一个关键思路是构建开放工程框架，使知识模块能有效嵌入具体应用场景。该框架应包含贡献者共同采纳的规范与社会机制。
原文摘要 · Abstract (English)
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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