Abstract
In the pre-demolition assessment, the regulated audit process at the heart of urban mining, 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 (XAI) techniques and domain knowledge graphs (KG) each address parts of this requirement, and existing taxonomies have cataloged their integration. The literature is descriptively rich but structurally under-specified: what remains less developed is a structural account of why specific integrations produce artifacts 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 artifacts and knowledge-graph substrate structures. Each mode unlocks a distinct property of defensibility and contributes to the kind of regulatory artifact 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.
Blogger's Review: This paper delves into the complementarity of knowledge graphs and explainable AI in urban mining, emphasizing the defensibility of decision support. It provides fresh perspectives and integration modes, laying a theoretical foundation for future urban mining practices.