PulseAugur
EN
LIVE 12:41:02

Knowledge Graphs and Explainable AI Enhance Urban Mining Defensibility

By PulseAugur Editorial · [2 sources] ·

This paper explores how knowledge graphs and explainable AI (XAI) can work together to improve urban mining processes. The authors propose four integration modes—Lifting, Constraining, Typing, and Revising—to enhance the defensibility of decisions made during pre-demolition assessments. These modes aim to create regulatory artifacts that neither knowledge graphs nor XAI can provide alone, with a fire-door example from urban mining illustrating their application using the W3C Linked Building Data stack. AI

IMPACT Proposes novel integration methods for XAI and knowledge graphs to improve decision-making in specialized domains like urban mining.

RANK_REASON Academic paper proposing new integration methods for existing AI techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Knowledge Graphs and Explainable AI Enhance Urban Mining Defensibility

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jan Gronewald, Andreas Emrich, Nijat Mehdiyev ·

    Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining

    arXiv:2607.09578v1 Announce Type: new 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 va…

  2. arXiv cs.AI TIER_1 English(EN) · Nijat Mehdiyev ·

    Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining

    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 de…

RELATED ENTITIES

RELATED TOPICS