Al-Bayan: A Knowledge-based System for Arabic Answer Selection

This paper describes Al-Bayan team participa-tion in SemEval-2015 Task 3, Subtask A. Task 3 targets semantic solutions for answer selec-tion in community question answering systems. We propose a knowledge-based solution for answer selection of Arabic questions, special-ized for Islamic sciences. We build a Semantic Interpreter to evaluate the semantic similarity between Arabic question and answers using our Quranic ontology of concepts. Using su-pervised learning, we classify the candidate answers according to their relevance to the users questions. Results show that our system achieves 74.53 % accuracy which is compara-ble to the other participating systems.