Computational methodologies for sanad-based hadith analysis: a review.
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| Title: | Computational methodologies for sanad-based hadith analysis: a review. |
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| Authors: | Mhamedi, Abdelilah1 abdelilah.mhamedi@etu.uae.ac.ma, Mghari, Mohammed1 mohammed.mghari@uae.ac.ma, El Hibaoui, Abdelaaziz1 aelhibaoui@uae.ac.ma |
| Source: | Telkomnika. Jun2026, Vol. 24 Issue 3, p840-851. 12p. |
| Subjects: | Hadith, Artificial intelligence, Deep learning, Text mining, Machine learning, Natural language processing |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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