Large Language Models in Open Government Data Analysis: A Systematic Mapping Study

Alberto Luciano de Souza Bastos, Luiz Felipe Cirqueira dos Santos, Shexmo Richarlison Ribeiro dos Santos, Marcus Vinicius Santana Silva, Marcos Cesar Barbosa dos Santos, Marcos Venicius Santos, Marckson Fábio da Silva Santos, Mariano Florencio Mendonça, Fabio Gomes Rocha

2025

Abstract

Background: The convergence of Large Language Models (LLMs) and open government data presents transformative potential for public administration, yet there exists a significant gap in understanding adoption patterns in this emerging domain. Aim: This study analyzes adoption patterns of Large Language Models in open government data analysis, characterizing researchers’ perceptions about benefits, limitations, and methodological implications. Method: We conducted a systematic mapping study following Petersen et al. (2008) guidelines, searching six academic databases. After screening, 24 primary studies were analyzed covering contribution types, validation methods, government domains, and LLM models. Results: Analysis revealed GPT model family predominance, with health as priority domain (4 studies), followed by security and justice (3 studies each). Conversational interfaces and information extraction were dominant functions (9 studies each). Conclusions: The field demonstrates evolution toward hybrid solutions integrating LLMs with structured knowledge resources. Consistent challenges across technologies-ethical issues, privacy concerns, and data quality-indicate the need for unified frameworks. Future research should focus on developing practical solutions to achieve technical maturity comparable to established software engineering fields.

Paper Citation

in Harvard Style

Bastos A., Cirqueira dos Santos L., Ribeiro dos Santos S., Silva M., Barbosa dos Santos M., Santos M., Santos M., Mendonça M. and Rocha F. (2025). Large Language Models in Open Government Data Analysis: A Systematic Mapping Study. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 404-411. DOI: 10.5220/0013777300003985

in Bibtex Style

@conference{webist25,
author={Alberto Bastos and Luiz Cirqueira dos Santos and Shexmo Ribeiro dos Santos and Marcus Silva and Marcos Barbosa dos Santos and Marcos Santos and Marckson Santos and Mariano Mendonça and Fabio Rocha},
title={Large Language Models in Open Government Data Analysis: A Systematic Mapping Study},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={404-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013777300003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Large Language Models in Open Government Data Analysis: A Systematic Mapping Study
SN - 978-989-758-772-6
AU - Bastos A.
AU - Cirqueira dos Santos L.
AU - Ribeiro dos Santos S.
AU - Silva M.
AU - Barbosa dos Santos M.
AU - Santos M.
AU - Santos M.
AU - Mendonça M.
AU - Rocha F.
PY - 2025
SP - 404
EP - 411
DO - 10.5220/0013777300003985
PB - SciTePress