用知识图谱与可解释AI互补，提升城市采矿评估的决策可信度。
Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
- 提出四种融合模式，让AI与知识图谱协同增强决策可解释性。
- 每种模式解锁不同可信属性，如溯源性、可辩驳性，满足监管要求。
- 适用于需要合规决策支持的城市采矿审计场景。
预拆除评估是城市采矿的核心监管流程，依赖人工智能辅助，但最终决策仍由专业审计人员负责。价值单位不仅是预测准确率，更在于决策的可辩护性：即透明性、合理性、来源可查和可争议性。可解释AI与领域知识图谱分别解决部分需求，现有文献虽已分类整合方式，但缺乏结构化解释——为何特定融合能产生单一资源无法实现的成果。本文基于信息系统资源基础理论，提出互补性理论框架，定义四种整合模式（提升、约束、类型化、修正），每种为对XAI产物与知识图谱结构的类型化操作，分别激活不同的可辩护属性，共同支撑监管所需决策产物。以建筑数据栈和估值扩展为例，展示火门评估中的应用实例。
原文摘要 · Abstract (English)
Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accountable for the decisions taken. The relevant unit of value is not prediction accuracy alone, but the defensibility of the supported decisions: their legibility, plausibility, sourcing, and contestability. Explainable AI techniques and domain knowledge graphs each address parts of this requirement, and existing taxonomies have catalogued their integration. The literature is descriptively rich but structurally under-specified: what remains less developed is a structural account of why specific integrations produce artefacts neither resource can provide alone. This paper offers a complementarity-theoretic interpretation grounded in the IS resource-based tradition. We propose four consolidated KG-XAI integration modes (Lifting, Constraining, Typing, and Revising), each defined as a typed operation over XAI artefacts and knowledge-graph substrate structures. Each mode unlocks a distinct property of defensibility and contributes to the kind of regulatory artefact pre-demolition assessment demands. A fire-door example from the urban-mining process illustrates the modes using the W3C Linked Building Data stack and valuation extensions.
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