We present KNOW--the Knowledge Navigator Ontology for the World--the first
ontology designed to capture everyday knowledge to augment large language
models (LLMs) in real-world generative AI use cases such as personal AI
assistants. Our domain is human life, both its everyday concerns and its major
milestones. We have limited the initial scope of the modeled concepts to only
established human universals: spacetime (places, events) plus social (people,
groups, organizations). The inclusion criteria for modeled concepts are
pragmatic, beginning with universality and utility. We compare and contrast
previous work such as Schema.org and Cyc--as well as attempts at a synthesis of
knowledge graphs and language models--noting how LLMs already encode internally
much of the commonsense tacit knowledge that took decades to capture in the Cyc
project. We also make available code-generated software libraries for the 12
most popular programming languages, enabling the direct use of ontology
concepts in software engineering. We emphasize simplicity and developer
experience in promoting AI interoperability.

我们介绍了知识导航本体（知识世界导航本体），这是第一个旨在捕捉日常知识以增强大型语言模型（LLM）在个人 AI 助手等实际生成 AI 应用场景中使用的本体。我们的领域是人类生活，包括日常关注和重要里程碑。我们仅模拟既定的人类普遍性概念：时空（地点、事件）和社交（人员、群体、组织）。模拟概念的包含标准是实用主义的，从普遍性和实用性开始。我们比较并对比了先前的工作，比如 Schema.org 和 Cyc，以及知识图谱和语言模型的综合尝试，指出 LLM 已经在内部编码了花费数十年时间才在 Cyc 项目中捕捉到的常识性知识。我们还提供了用于 12 种最流行编程语言的由代码生成的软件库，以便在软件工程中直接使用本体概念。我们强调简单性和开发者体验，以促进人工智能的互操作性。