LLMs and Knowledge Discovery in Low-Resource Language Parliamentary Corpora: The PQ Dashboard Case Study

Joel Azzopardi

2025

Abstract

Parliamentary Questions (PQs) are a critical mechanism for democratic oversight and accountability. However, their comprehensive analysis can be hindered by limitations such as single-language availability (especially when the language is a low-resource language such as Maltese) and a lack of structured thematic organisation or interlinking. This paper introduces the PQ Dashboard, a web-based platform developed to enhance the accessibility and analytical utility of Maltese Parliamentary Questions. The system employs AI and open Large Language Models (LLMs) to automate PQ collection, translate content into English, classify it according to the COFOG-99 taxonomy, extract key terms, and identify interconnections. The interactive dashboard provides users – including the public, journalists, and academic researchers – with functionalities to navigate PQs by category or keyword, visualise thematic distributions, and analyse trends in MPs’ activity and ministerial responses. This enhanced data accessibility aims to facilitate deeper insights into parliamentary discourse, policy development, and governmental accountability. The PQ Dashboard demonstrates a practical application of AI-driven solutions for transforming unstructured public data into a more accessible and analysable format, thereby contributing to increased transparency and informed public engagement.

Paper Citation

in Harvard Style

Azzopardi J. (2025). LLMs and Knowledge Discovery in Low-Resource Language Parliamentary Corpora: The PQ Dashboard Case Study. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 159-170. DOI: 10.5220/0013835100004000

in Bibtex Style

@conference{kdir25,
author={Joel Azzopardi},
title={LLMs and Knowledge Discovery in Low-Resource Language Parliamentary Corpora: The PQ Dashboard Case Study},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={159-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013835100004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - LLMs and Knowledge Discovery in Low-Resource Language Parliamentary Corpora: The PQ Dashboard Case Study
SN -
AU - Azzopardi J.
PY - 2025
SP - 159
EP - 170
DO - 10.5220/0013835100004000
PB - SciTePress