Abstract: Accurate statistical information is critical for understanding, describing, and managing socio-economic systems. While data availability has increased, often it does not meet the quality requirements for effective governance. Administrative registers are crucial for statistical information production, but their potential is hampered by quality issues stemming from administrative inconsistencies. This paper explores the integration of semantic technologies, including ontologies and knowledge graphs, with administrative databases to improve data quality. We discuss the development of large language models (LLMs) that enable a robust, queryable framework, facilitating the integration of disparate data sources. This approach ensures high-quality administrative data, essential for statistical reuse and the development of comprehensive, dynamic knowledge graphs and LLMs tailored for administrative applications.

Adham Kahlawi and Cristina Martelli. “Enhancing Administrative Source Registers for the Development of a Robust Large Language Model: A Novel Methodological Approach”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150702

@article{Kahlawi2024,
title = {Enhancing Administrative Source Registers for the Development of a Robust Large Language Model: A Novel Methodological Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150702},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150702},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Adham Kahlawi and Cristina Martelli}
}