Computational methodologies for sanad-based hadith analysis: a review.
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| Title: | Computational methodologies for sanad-based hadith analysis: a review. |
|---|---|
| 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] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195172772 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Computational methodologies for sanad-based hadith analysis: a review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mhamedi%2C+Abdelilah%22">Mhamedi, Abdelilah</searchLink><relatesTo>1</relatesTo><i> abdelilah.mhamedi@etu.uae.ac.ma</i><br /><searchLink fieldCode="AR" term="%22Mghari%2C+Mohammed%22">Mghari, Mohammed</searchLink><relatesTo>1</relatesTo><i> mohammed.mghari@uae.ac.ma</i><br /><searchLink fieldCode="AR" term="%22El+Hibaoui%2C+Abdelaaziz%22">El Hibaoui, Abdelaaziz</searchLink><relatesTo>1</relatesTo><i> aelhibaoui@uae.ac.ma</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Jun2026, Vol. 24 Issue 3, p840-851. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hadith%22">Hadith</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12928/TELKOMNIKA.v24i3.27447 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 840 Subjects: – SubjectFull: Hadith Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Text mining Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Natural language processing Type: general Titles: – TitleFull: Computational methodologies for sanad-based hadith analysis: a review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mhamedi, Abdelilah – PersonEntity: Name: NameFull: Mghari, Mohammed – PersonEntity: Name: NameFull: El Hibaoui, Abdelaaziz IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 3 Titles: – TitleFull: Telkomnika Type: main |
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