چکیده (انگلیسی):

In spite of great efforts that have been made to present systems
that support the user's need of the answers from the Holy Quran, the current
systems of English translation of Quran still need to do more investigation
in order to develop the process of retrieving the accurate verse based on
user's question. The Islamic terms are different from one document to
another and might be undefined for the user. Thus, the need emerged for a
Question Answering System (QAS) that retrieves the exact verse based on
a semantic search of the Holy Quran. The main objective of this research is
to develop the efficiency of the information retrieval from the Holy Quran
based on QAS and retrieving an accurate answer to the user's question
through classifying the verses using the Neural Network (NN) technique
depending on the purpose of the verses' contents, in order to match between
questions and verses. This research has used the most popular English
translation of the Quran of Abdullah Yusuf Ali as the data set. In that
respect, the QAS will tackle these problems by expanding the question,
using WordNet and benefitting from the collection of Islamic terms in order
to avoid differences in the terms of translations and question. In addition,
this QAS classifies the Al-Baqarah surah into two classes, which are
Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of
irrelevant verses since the user's questions are asking for Fasting and
Pilgrimage. Hence, this QAS retrieves the relevant verses to the question
based on the N-gram technique, then ranking the retrieved verses based on
the highest score of similarity to satisfy the desire of the user. According to
F-measure, the evaluation of classification by using NN has shown an
approximately 90% level and the evaluation of the proposed approach of
this research based on the entire QAS has shown an approximately 87%
level. This demonstrates that the QAS succeeded in providing a promising
outcome in this critical field.