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AI Research
arXiv CS.AI·23d ago·5 min read
Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
TL;DR
Urban mining faces challenges in data integration and interpretation. By combining knowledge graphs with explainable AI techniques, a novel framework was developed to enhance data usability.
✦ Why It Matters
Engineers can implement knowledge graphs and XAI in their urban mining projects to enhance data interpretation and decision-making.
Key Takeaways
- Explore the four integration modes: Lifting, Constraining, Typing, and Revising.
- Utilize knowledge graphs to improve data sourcing and clarity.
- Apply explainable AI techniques to enhance decision plausibility.
- Incorporate regulatory requirements into your urban mining assessments.
How It Works
The integration of knowledge graphs and explainable AI is structured around four modes that enhance decision-making. Lifting involves augmenting XAI outputs with KG data, Constraining limits XAI outputs based on KG rules, Typing categorizes data for better interpretation, and Revising updates KG based on XAI insights.
Each mode serves to improve the transparency and accountability of decisions made during urban mining assessments.