知识工程(Knowledge Engineering)自诞生以来,经历了从专家系统、语义网到大规模知识图谱的演进。然而,在当前的数智化时代,传统知识工程面临着本体构建成本极高、知识表征静态僵化、以及与大模型(LLM)结合时的逻辑断层与幻觉泛滥等结构性瓶颈。本文引入“道谱学”(Daopology)的元学科视角,对知识工程进行了彻底的本体重构。 本文提出:知识的核心并非孤立的实体或三元组堆砌,而是具备内在生成元与拓扑连通性的“结构核(Kernel)”。传统知识工程受限于连续概率拟合或单一符号推演的局限,丢失了知识的网络结构属性;而道谱学将知识库重构为一个多层级离散拓扑图,利用离散结构退火(DSA)进行推演,利用可修正结构智能(CSI)实现知识的局部重写。本文详细阐述了该重构范式下的知识提取、拓扑构建、符号约束与动态演化机制,为下一代白盒化、可审计、具备强推理能力的知识工程提供了全新的理论基石与工程落地路径。 Since its inception, Knowledge Engineering has evolved from expert systems and the Semantic Web to large-scale knowledge graphs. However, in the current era of digital intelligence, traditional knowledge engineering faces structural bottlenecks: extremely high ontology construction costs, rigid static knowledge representations, and logical discontinuities or rampant hallucinations when integrated with Large Language Models (LLMs). This paper introduces the meta-disciplinary perspective of Daopology to fundamentally reconstruct the ontological foundation of knowledge engineering. We propose that the core of knowledge is not isolated entities or a stack of triples, but rather a Structural Kernel with intrinsic generators and topological connectivity. Traditional knowledge engineering is limited by continuous probabilistic fitting or single symbolic deduction, losing the network structural properties of knowledge. Daopology reconstructs the knowledge base into a multi-level discrete topological graph, utilizing Discrete Structural Annealing (DSA) for deduction and Correctable Structural Intelligence (CSI) for local knowledge rewriting. This paper elaborates on the mechanisms of knowledge extraction, topology construction, symbolic constraints, and dynamic evolution under this reconstructed paradigm, providing a novel theoretical cornerstone and engineering implementation path for the next generation of white-box, auditable, and highly reasoning-capable knowledge engineering.