Multi-IsnadSet MIS for Sahih Muslim Hadith with chain of narrators, based on multiple ISNAD

In the Islamic domain, Hadiths hold significant importance, standing as crucial texts following the Holy Quran. Each Hadith contains three main parts: the ISNAD (chain of narrators), TARAF (starting part, often from Prophet Muhammad), and MATN (Hadith content). ISNAD, a chain of narrators involved i...

Full description

Saved in:

Bibliographic Details
Published in:	Data in brief Vol. 54; p. 110439
Main Authors:	Farooqi, Aziz Mehmood, Malick, Rauf Ahmed Shams, Shaikh, Muhammad Shahzad, Akhunzada, Adnan
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 01-06-2024 Elsevier
Subjects:	Chain of narrators Data Graph database Graph Neural Networks (GNN) Machine learning Multi-Isnad of Hadith Narrators dataset Social-Network Analysis (SNA) Spatial–Temporal data Social-Network Analysis (SNA) Multi-Isnad of Hadith Narrators dataset Spatial–Temporal data Graph Neural Networks (GNN) Machine learning Chain of narrators Graph database
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	In the Islamic domain, Hadiths hold significant importance, standing as crucial texts following the Holy Quran. Each Hadith contains three main parts: the ISNAD (chain of narrators), TARAF (starting part, often from Prophet Muhammad), and MATN (Hadith content). ISNAD, a chain of narrators involved in transmitting that particular MATN. Hadith scholars determine the trustworthiness of the transmitted MATN by the quality of the ISNAD. The ISNAD's data is available in its original Arabic language, with narrator names transliterated into English. This paper presents the Multi-IsnadSet (MIS), that has great potential to be employed by the social scientist and theologist. A multi-directed graph structure is used to represents the complex interactions among the narrators of Hadith. The MIS dataset represent directed graph which consists of 2092 nodes, representing individual narrators, and 77,797 edges represent the Sanad-Hadith connections. The MIS dataset represents multiple ISNAD of the Hadith based on the Sahih Muslim Hadith book. The dataset was carefully extracted from online multiple Hadith sources using data scraping and web crawling techniques tools, providing extensive Hadith details. Each dataset entry provides a complete view of a specific Hadith, including the original book, Hadith number, textual content (MATN), list of narrators, narrator count, sequence of narrators, and ISNAD count. In this paper, four different tools were designed and constructed for modeling and analyzing narrative network such as python library (NetworkX), powerful graph database Neo4j and two different network analysis tools named Gephi and CytoScape. The Neo4j graph database is used to represent the multi-dimensional graph related data for the ease of extraction and establishing new relationships among nodes. Researchers can use MIS to explore Hadith credibility including classification of Hadiths (Sahih=perfection in the Sanad/Dhaif=imperfection in the Sanad), and narrators (trustworthy/not). Traditionally, scholars have focused on identifying the longest and shortest Sanad between two Narrators, but in MIS, the emphasis shifts to determining the optimum/authentic Sanad, considering narrator qualities. The graph representation of the authentic and manually curated dataset will open ways for the development of computational models that could identify the significance of a chain and a narrator. The dataset allows the researchers to provide Hadith narrators and Hadith ISNAD that could be used in a wide variety of future research studies related to Hadith authentication and rules extraction. Moreover, the dataset encourages cross-disciplinary research, bridging the gap between Islamic studies, artificial intelligence (AI), social network analysis (SNA), and Graph Neural Network (GNN).
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2352-3409 2352-3409
DOI:	10.1016/j.dib.2024.110439