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[2602.00012] OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
Summary
The OGD4All framework enhances citizen interaction with geospatial Open Government Data using Large Language Models, achieving high accuracy and minimizing data hallucination.
Why It Matters
As governments increasingly rely on open data, frameworks like OGD4All are crucial for making this data accessible and reliable. By leveraging LLMs, it promotes transparency and trust in public data usage, which is essential for informed citizen engagement and governance.
Key Takeaways
- OGD4All achieves 98% analytical correctness and 94% recall in data retrieval.
- The framework minimizes hallucination risks by rejecting unsupported questions.
- It combines semantic data retrieval with agentic reasoning for enhanced user interaction.
- Statistical robustness tests confirm the reliability of the framework.
- Promotes trustworthy AI applications in open governance.
Computer Science > Machine Learning arXiv:2602.00012 (cs) [Submitted on 30 Nov 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models Authors:Michael Siebenmann, Javier Argota Sánchez-Vaquerizo, Stefan Arisona, Krystian Samp, Luis Gisler, Dirk Helbing View a PDF of the paper titled OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models, by Michael Siebenmann and 5 other authors View PDF HTML (experimental) Abstract:We present OGD4All, a transparent, auditable, and reproducible framework based on Large Language Models (LLMs) to enhance citizens' interaction with geospatial Open Government Data (OGD). The system combines semantic data retrieval, agentic reasoning for iterative code generation, and secure sandboxed execution that produces verifiable multimodal outputs. Evaluated on a 199-question benchmark covering both factual and unanswerable questions, across 430 City-of-Zurich datasets and 11 LLMs, OGD4All reaches 98% analytical correctness and 94% recall while reliably rejecting questions unsupported by available data, which minimizes hallucination risks. Statistical robustness tests, as well as expert feedback, show reliability and social relevance. The proposed approach shows how LLMs can provide explainable, multimodal access to public data, advancing trustworthy AI for open governance. ...
