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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mohammed Mghari
Abdelmalek Essaâdi University
Computer Science & IT

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Retrieval-augmented generation for Arabic legal information: the family code case study Jamal Hrimech; Mohammed Mghari; Youssef Zaz
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27400

Abstract

This document describes the implementation and evaluation of a retrieval-augmented generation (RAG) system to improve access to and understanding of Moroccan law, particularly the family code in Arabic. The research addresses the drawbacks of the widely used linguistic model applied to complex legal terminology in Arabic and aims to help citizens access crucial legal data. We built a new custom dataset with 2.5 k question-answer pairs while preprocessing and using the BGE-m3 embedding model in this experiment. Performance metrics, such as mean reciprocal rank (MRR), Recall@k, and F1-score, indicate that the RAG approach is effective compared to the use of standalone large language models (LLMs). Moreover, an evaluation on metrics such as the blue score, fidelity, response relevance, and contextual relevance indicated that the matching of meanings and context were well captured, which signifies a very good semantic understanding. The research highlights the need for language-specific model specialization in Arabic and presents its main challenges, such as dialectal variations and appropriate evaluation measures. The results indicate that well-developed RAG systems offer a promising approach to improving access to legal information in Arabic-speaking practice communities and to guiding future research and development in this field.
Computational methodologies for sanad-based hadith analysis: a review Abdelilah Mhamedi; Mohammed Mghari; Abdelaaziz El Hibaoui
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27447

Abstract

Hadith literature, a cornerstone of Islamic tradition, critically depends on the sanad (chain of narrators) for authentication, a process traditionally requiring profound scholarly expertise. This paper presents a systematic review of computational methodologies designed to enhance and automate sanad analysis, bridging Islamic studies with advanced artificial intelligence (AI). We categorize progress across four key domains: automated authenticity classification, sophisticated narrator network analysis, textual information extraction (e.g., named entity recognition), and the development of specialized datasets and ontologies. Our findings reveal a significant paradigm shift from rule-based systems to advanced machine learning (ML) and deep learning (DL) techniques. This review synthesizes contributions from over 50 studies, highlighting critical challenges including data scarcity, narrator disambiguation, and cross-linguistic resource limitations. We emphasize the novelty of this cross-domain synthesis and discuss how these intelligent systems can be integrated into digital Islamic archives, low-resource mobile hadith applications, and embedded natural language processing (NLP) engines. This work charts a course for future research to develop more robust, scalable, and ethically grounded computational tools, complementing traditional hadith scholarship with advanced engineering solutions
Co-Authors Abdelaaziz El Hibaoui Abdelilah Mhamedi Jamal Hrimech Youssef Zaz