Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining

2026-07-10

AI Summary

This study addresses the lack of defensibility—encompassing interpretability, reasonableness, traceability, and contestability—in pre-demolition assessment decisions within urban mining. To tackle this challenge, the authors integrate knowledge graphs (KGs) with explainable artificial intelligence (XAI), drawing on complementarity theory to systematically propose four structured integration patterns: Lifting, Constraining, Typing, and Revising. Grounded in the W3C Linked Data stack, domain ontologies, and XAI techniques, the approach generates regulatory-compliant decision-support artifacts. Validation through a fire door case study demonstrates that these patterns effectively produce assessment outcomes that are explainable, traceable, and structurally defensible—capabilities unattainable by either KGs or XAI alone.

Abstract

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.

Problem

• Knowledge Graphs
• Explainable AI
• Urban Mining
• Pre-demolition Assessment
• Defensibility

Innovation

• Knowledge Graph
• Explainable AI
• Urban Mining
• Complementarity Theory
• Decision Defensibility