Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
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.
- ▪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.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.09578 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2607.09578 (cs) [Submitted on 10 Jul 2026] Title:Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining Authors:Jan Gronewald, Andreas Emrich, Nijat Mehdiyev View a PDF of the paper titled Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining, by Jan Gronewald and 1 other authors View PDF HTML (experimental) 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.
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