重新定义知识建模设计，提出数据管道驱动的新实践路径。
Broadening Ontologization Design: Embracing Data Pipeline Strategies
- 以数据管道重构知识建模流程，突破传统方法局限。
- 结合30年研究积累，展示bCLEARer方法在新实践中的应用效果。
- 从数字演化视角出发，为知识建模提供战略级指导框架。
本文旨在揭示知识建模（ontologization）的设计空间远比当前实践所呈现的更为广阔。我们指出，工程流程与产品均需系统性设计，并识别出其中的关键构成要素。通过研究三十多年来基于异常方法bCLEARer的实践，本文探索了将一系列根本性新实践以数据管道形式实现的可能性。同时提出，为知识建模设定演化背景有助于更好地理解这些新实践的本质，并构建起支撑创造性过程的概念骨架。该演化视角将数字化视为信息演进长链中最新的一环，从而将知识建模重新定位为利用数字化机遇的战略工具。
原文摘要 · Abstract (English)
Our aim in this paper is to outline how the design space for the ontologization process is broader than current practice would suggest. We point out that engineering processes as well as products need to be designed and identify some components of the design. We investigate the possibility of designing a range of radically new practices implemented as data pipelines, providing examples of the new practices from our work over the last three decades with an outlier methodology, bCLEARer. We also suggest that setting an evolutionary context for ontologization helps one to better understand the nature of these new practices and provides the conceptual scaffolding that shapes fertile processes. Where this evolutionary perspective positions digitalization (the evolutionary emergence of computing technologies) as the latest step in a long evolutionary trail of information transitions. This reframes ontologization as a strategic tool for leveraging the emerging opportunities offered by digitalization.
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