发表机构
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究城市采矿中拆除前评估，基于信息系统资源传统，提出知识图谱与可解释人工智能的四种整合模式，解锁决策可辩护性属性，以满足拆除前评估对监管工件的需求，通过防火门示例说明模式。
AI 中文摘要
拆除前评估是城市采矿核心的规范审核流程，是一个信息过程，其中人工智能支持必须服务于对所做决策负责的合格审核员。相关价值单位不仅是预测准确性，还包括所支持决策的可辩护性。可解释人工智能技术和领域知识图谱各自满足了部分要求，现有分类法已对它们的整合进行了编目。本文基于信息系统资源传统提供了一种互补理论解释，提出了四种整合模式，每个模式都解锁了一种独特的可辩护性属性，有助于满足拆除前评估所需的监管工件类型。城市采矿过程中的防火门示例说明了这些模式。
英文摘要
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.
CommentsAccepted for presentation at the AISE Workshop @ IJCAI-ECAI 2026
