A Novel LLM-Based Approach for Automated Seerah-Hadith Mapping: Connecting Islamic Historical Narratives Through Vector Search and Semantic Analysis
Abstract
Seerah and Hadith are essential sources of Islamic knowledge, but there has been limited research on systematically linking these two areas. This paper introduces the "Seerah-Hadith Mapping" project, which uses Large Language Models (LLMs) to map related passages between Seerah and Hadith. By adding new connections between these texts, this approach builds on existing scholarship and helps make Islamic knowledge more accessible to those without specialized knowledge in Islamic studies.
Cite
Text
Talha et al. "A Novel LLM-Based Approach for Automated Seerah-Hadith Mapping: Connecting Islamic Historical Narratives Through Vector Search and Semantic Analysis." NeurIPS 2024 Workshops: MusIML, 2024.Markdown
[Talha et al. "A Novel LLM-Based Approach for Automated Seerah-Hadith Mapping: Connecting Islamic Historical Narratives Through Vector Search and Semantic Analysis." NeurIPS 2024 Workshops: MusIML, 2024.](https://mlanthology.org/neuripsw/2024/talha2024neuripsw-novel/)BibTeX
@inproceedings{talha2024neuripsw-novel,
title = {{A Novel LLM-Based Approach for Automated Seerah-Hadith Mapping: Connecting Islamic Historical Narratives Through Vector Search and Semantic Analysis}},
author = {Talha, Mushfiqur Rahman and Shams, Mohammad Galib and Islam, Riasat and Mosharraf, Nabil},
booktitle = {NeurIPS 2024 Workshops: MusIML},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/talha2024neuripsw-novel/}
}