让领域成为知识图谱的核心，实现动态上下文推理。
Domain-Contextualized Concept Graphs: A Computable Framework for Knowledge Representation
- 用概念-关系@领域-概念三元组建模，领域可动态定义
- 支持跨领域类比和个性化知识推理，效果优于传统框架
- 适用于教育、企业知识库等需要灵活理解的场景
传统知识图谱受限于固定本体，概念被僵化地组织在层级结构中。根本原因在于将领域视为隐含上下文而非显式推理单元。为此，我们提出领域情境化概念图（CDC），一种新型知识建模框架，将领域提升为概念表征的一等元素。CDC采用C-D-C三元组结构 <概念, 关系@领域, 概念'>，其中领域作为按需定义的动态分类维度。基于认知语言学同构映射原则，CDC实现了人类通过上下文框架理解概念的方式。我们形式化了二十余种标准化关系谓词（结构、逻辑、跨领域、时间等），并在Prolog中实现以支持完整推理。教育、企业知识系统与技术文档等案例研究显示，CDC能实现上下文感知推理、跨领域类比与个性化知识建模，这是传统本体框架无法实现的能力。
原文摘要 · Abstract (English)
Traditional knowledge graphs are constrained by fixed ontologies that organize concepts within rigid hierarchical structures. The root cause lies in treating domains as implicit context rather than as explicit, reasoning-level components. To overcome these limitations, we propose the Domain-Contextualized Concept Graph (CDC), a novel knowledge modeling framework that elevates domains to first-class elements of conceptual representation. CDC adopts a C-D-C triple structure - <Concept, Relation@Domain, Concept'> - where domain specifications serve as dynamic classification dimensions defined on demand. Grounded in a cognitive-linguistic isomorphic mapping principle, CDC operationalizes how humans understand concepts through contextual frames. We formalize more than twenty standardized relation predicates (structural, logical, cross-domain, and temporal) and implement CDC in Prolog for full inference capability. Case studies in education, enterprise knowledge systems, and technical documentation demonstrate that CDC enables context-aware reasoning, cross-domain analogy, and personalized knowledge modeling - capabilities unattainable under traditional ontology-based frameworks.
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