Using Textual Case-based Reasoning in Intelligent Fatawa QA System

Textual Case-Based Reasoning (TCBR) is an artificial intelligence approach to problem solving and learning in which textual expertise is collected in a library of past cases. One of the critical application domains is the Islamic Fatawa (religious verdict) domain, which refers to seeking a legal ruling for religious issues that Muslims all over the globe pose on a daily basis. Official religious organizations like Egypt's Dar al-Ifta(1) is responsible for receiving and answering people's religious inquiries daily. Due to the enormous number of inquiries Dar al-Ifta receives every day, it cannot he handled at the same time. This task actually requires a certain smart system that can help in fulfilling people's needs for answers. However, applying TCBR in the domain of issuing Fatawa faces several challenges related to the language syntax and semantics. The contribution of this paper is to propose an intelligent fatwa Questions Answering (QA) system that can overcome the challenges and respond to a user's inquiry through providing semantically closest inquiries that previously answered. Moreover, the paper shows how the proposed system can learn when a new inquiry arrives. Finally, results will be discussed.