OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910032416735232 |
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| author | Siebenmann, Michael Sánchez-Vaquerizo, Javier Argota Arisona, Stefan Samp, Krystian Gisler, Luis Helbing, Dirk |
| author_facet | Siebenmann, Michael Sánchez-Vaquerizo, Javier Argota Arisona, Stefan Samp, Krystian Gisler, Luis Helbing, Dirk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models Siebenmann, Michael Sánchez-Vaquerizo, Javier Argota Arisona, Stefan Samp, Krystian Gisler, Luis Helbing, Dirk Machine Learning Artificial Intelligence Computers and Society Information Retrieval 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. |
| title | OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models |
| topic | Machine Learning Artificial Intelligence Computers and Society Information Retrieval |
| url | https://arxiv.org/abs/2602.00012 |