构建可复用的流程知识本体，让工业经验变得可管理、可共享。
Procedural Knowledge Ontology (PKO)
- 基于三个工业场景需求，重构现有本体构建流程知识模型。
- 支持流程知识全生命周期管理，提升跨系统互操作性。
- 适合需要挖掘和应用工业经验的AI与数据工具开发者。
流程、工作流和指南是保障工业企业正常运行的核心：工厂产线、设备或服务的顺利运作，往往依赖于员工积累的经验与专长。然而这种流程知识（PK）多为隐性存在，难以高效利用。本文提出PKO——流程知识本体，通过复用并扩展现有本体，实现对流程及其执行过程的显式建模。PKO基于三个异构工业用例的需求构建，可被任何依赖共享、互操作表示的AI与数据驱动工具使用，以支持流程知识全生命周期的治理。文中详述其结构与设计方法，并通过实际应用展示其在流程知识获取与利用中的相关性、质量与影响力。
原文摘要 · Abstract (English)
Processes, workflows and guidelines are core to ensure the correct functioning of industrial companies: for the successful operations of factory lines, machinery or services, often industry operators rely on their past experience and know-how. The effect is that this Procedural Knowledge (PK) remains tacit and, as such, difficult to exploit efficiently and effectively. This paper presents PKO, the Procedural Knowledge Ontology, which enables the explicit modeling of procedures and their executions, by reusing and extending existing ontologies. PKO is built on requirements collected from three heterogeneous industrial use cases and can be exploited by any AI and data-driven tools that rely on a shared and interoperable representation to support the governance of PK throughout its life cycle. We describe its structure and design methodology, and outline its relevance, quality, and impact by discussing applications leveraging PKO for PK elicitation and exploitation.
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