Omar Shafie, Kareem Darwish, and Bernard J. Jansen, Hamad Bin Khalifa University, Qatar

Hadith is the term used to describe the narration of the sayings and actions of Prophet Mohammad (p.b.u.h.). The study of Hadith can be modeled into a pipeline of tasks performed on a collection of textual data. Although many attempts have been made for developing Hadith search engines, existing solutions are repetitive, text-based, and manually annotated. This research documents 6 Hadith Retrieval methods, discusses their limitations, and introduces 2 methods for robust narrative retrieval. Namely, we address the challenge of user needs by reformulating the problem in a two-fold solution: declarative knowledge-graph querying; and semantic-similarity classification for Takhreej groups retrieving. The classifier was built by fine-tuning an AraBERT transformer model on a 200k pairs sample and scored 90% recall and precision. This work demonstrated how the Hadith Retrieval could be more ef icient and insightful with a user-centered methodology, which is an under-explored area with high potential.

Hadith, Knowledge-graphs, Arabic, Semantic Similarity