Robust Hadith IR using Knowledge-Graphs and Semantic-Similarity Classification

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.