Open AccessAutomated Islamic Jurisprudential Legal Opinions Generation Using Artificial Intelligence
Author(s) -
Amr Abdullah Munshi,
Wesam Hasan AlSabban,
Abdullah Tarek Farag,
Omar Essam Rakha,
Ahmad A. Al Sallab,
Majid Alotaibi
Publication year - 2022
Publication title -
pertanika journal of science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.174
H-Index - 15
eISSN - 2231-8526
pISSN - 0128-7680
DOI - 10.47836/pjst.30.2.16
Subject(s) - islam , sophistication , computer science , artificial intelligence , automation , sharia , data science , scalability , engineering , database , social science , sociology , geography , mechanical engineering , archaeology
Islam is the second-largest and fastest-growing religion. The Islamic Law, Sharia, represents a profound component of the day-to-day lives of Muslims. While sources of Sharia are available for anyone, it often requires a highly qualified person, the Mufti, to provide Fatwa. With Islam followers representing almost 25% of the planet earth population, generating many queries, and the sophistication of the Mufti qualification process, creating a shortage in them, we have a supply-demand problem, calling for Automation solutions. This scenario motivates the application of Artificial Intelligence (AI) to Automated Islamic Fatwa in a scalable way that can adapt to various sources like social media. In this work, the potential of AI, Machine Learning, and Deep Learning, with technologies like Natural Language Processing (NLP), paving the way to help the Automation of Islam Fatwa are explored. The work started by surveying the State-of-The-Art (SoTA) of NLP and exploring the potential use-cases to solve the problems of Question answering and Text Classification in the Islamic Fatwa Automation. The first and major enabler component for AI application for Islamic Fatwa, the data were presented by building the largest dataset for Islamic Fatwa, spanning the widely used websites for Fatwa. Moreover, the baseline systems for Topic Classification, Topic Modeling, and Retrieval-based Question-Answering are presented to set the future research and benchmark on the dataset. Finally, the dataset is released and baselines to the public domain to help advance future research in the area.