![]() ![]() ![]() Then, the second algorithm is used for developing difference words from that root using Arabic morphology template, the morphology developing algorithm develops the Arabic word by formulating the roots according to the Arabic template. The approach provides three algorithms in the first algorithm Arabic word root is generated using the concept of permutation and combination, the root generator algorithm generates roots by applying permutations to the Arabic alphabetic letters. This paper presents a novel approach for Arabic root generation and lexicon development. The Quranic dataset presented in this paper was designed to be appropriate for: database, data mining, text mining and Artificial Intelligence applications it is also designed to serve as a comprehensive encyclopedia of holy Quran and the Quranic Science books. ![]() Also, the paper presents models of the dataset at all levels. The final dataset is represented in excel sheets and database records format. All these are linked with interpretations and meanings, parsing, translations, intonation roots and stems of words, all from authentic and reliable sources. Holy Quran text is transferred into structured multi-dimensional data records starting from the chapter level, the word level and then the character level. The paper presents the algorithms and approaches that have been designed to extract an aggregative data from massive Arabic text sources including the holy Quran and tightly associated books. In this context, this paper provides a comprehensive Quranic Dataset as a first part (foundation) of an ongoing research that attempts to lay grounds for approaches and applications to explore the holy Quran. On the other hand, automatically extracting reliable knowledge from specialized data sources as holy books is considered ultimately a challenging task but of great benefit to all humans. Arabic NLP is considered immature due to several reasons including the low available resources. Extracting knowledge from text documents has become one of the main hot topics in the field of Natural Language Processing (NLP) in the era of information explosion. ![]()
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