Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
For auditors in urban mining, this provides a structured framework to combine KG and XAI for more defensible decisions, though it is a conceptual contribution without empirical validation.
The paper proposes four integration modes (Lifting, Constraining, Typing, Revising) between knowledge graphs and explainable AI to enhance defensibility of AI-supported decisions in pre-demolition assessment for urban mining, illustrated with a fire-door example.
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.