A Decade in Computational Quranic Studies (2016–2025): A Systematic Survey of the Evolution from Statistical Methods to Large Language Models
DOI:
https://doi.org/10.59222/ustjet.4.1.2Keywords:
Computational Qur’anic studies, Arabic Natural Language Processing (NLP), Transformer Models, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Systematic review, digital humanitiesAbstract
The intersection of artificial intelligence and sacred scripture represents a critical yet underexplored frontier in the digital humanities. This paper presents the first decade-spanning, systematic survey of computational Qur’anic studies (2016–2025), tracing the field’s evolution from fragmented, ontology-based prototypes to a coherent, methodologically mature research discipline. Through a rigorous, multi-phase systematic review of 112 peer-reviewed studies, we map a transformative trajectory across three eras: a Foundational Era (2016–2019) focused on knowledge representation; a Transformer Turn (2020–2023) driven by community benchmarks and Arabic transformer models; and an Acceleration Era (2024–2025) marked by the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Our longitudinal analysis uncovers a clear paradigm shift—from rule-based and statistical methods toward deep architectures explicitly engineered for ethical and theological fidelity. While remarkable advances have been achieved in Qur’anic Question Answering, Verse Authentication, and Recitation Analysis, the field still faces major gaps in multilingual applications, computational modeling of canonical recitation traditions (Qira¯’a¯t), and the deep integration of classical tafs¯ır scholarship. By introducing a structured taxonomy, a diachronic synthesis of research trends, and a strategic roadmap for the next decade, this survey reframes computational Qur’anic studies as both a case study and a catalyst for innovation in responsible, explainable, and theologically grounded AI.
References
[1] S. E. Jones, The Emergence of the Digital Humanities. Routledge, 2021.
[2] Pew Research Center, “The global religious landscape: A report on the size and distribution of the world’s major religious groups as of 2010,” Pew Research Center, 2012.
[3] A. Sinno, Muslims in Western Politics. Indiana University Press, 2022.
[4] M. H. Bashir et al., “Arabic natural language processing for Qur’anic research: A systematic review,” Artificial Intelligence Review, vol. 56, no. 7, pp. 6801–6854, 2023.
[5] K. Shahzad and A. Ashraf, “Artificial intelligence (AI) and natural language processing (NLP) for religious texts—A systematic mapping study,” Journal of Integrated Sciences, vol. 5, no. 4, 2025.
[6] M. Alqahtani and E. Atwell, “Arabic Quranic search tool based on ontology,” in Proc. 21st Int. Conf. on Applications of Natural Language to Information Systems (NLDB 2016), 2016, pp. 478–485.
[7] A. S. M. Rusli, F. Ridzuan, Z. M. Zaki, M. Sayuti, and M. S. Abdullah, “A systematic review on semantic-based ontology for Quranic knowledge,” International Journal of Engineering & Technology, vol. 7, no. 2.29, pp. 151–157, 2018.
[8] R. Malhas, W. Mansour, and T. Elsayed, “Qur’an QA 2022: Overview of the first shared task on question answering over the Holy Qur’an,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 79–87.
[9] S. S. M. Alnefaie, “Artificial intelligence for answering questions from the Holy Quran and Hadith,” Ph.D. dissertation, Univ. of Leeds, 2024.
[10] D. Akra, T. Hammouda, and M. Jarrar, “QuranMorph: Morphologically annotated Quranic corpus,” arXiv preprint arXiv:2506.18148, 2025.
[11] A. M. Mustafa, S. Nakhleh, and R. Irsheidat, “Interpreting Arabic transformer models: A study on XAI interpretability for Qur’anic semantic-search models,” Jordanian Journal of Computers and Infor- mation Technology, vol. 10, no. 3, pp. 312–327, 2024.
[12] B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” Keele Univ. and Durham Univ. Joint Report, 2007.
[13] K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conduct- ing systematic mapping studies in software engineering: An update,” Information and Software Technology, vol. 64, pp. 1–18, 2015.
[14] C. Wohlin, “Guidelines for snowballing in systematic literature studies and a replication in software engineering,” in Proc. 18th Int. Conf. on Evaluation and Assessment in Software Engineering, 2014, Art. 38, pp. 1–10.
[15] A. Ta’a, Q. A. Abed, B. M. Ali, and M. Ahmad, “Ontology-based approach for knowledge retrieval in Al-Quran holy book,” International Journal of Computer Engineering Research, vol. 6, no. 3, pp. 8–15, 2016.
[16] R. Ahmed and E. Atwell, “Developing an ontology of concepts in the Qur’an,” International Journal on Islamic Applications in Computer Science and Technology, vol. 4, no. 4, pp. 1–8, 2016.
[17] M. F. Noordin, T. M. T. Sembok, R. Othman, and R. H. Gusmita, “Con- structing an ontology-based and graph-based knowledge representation of English Quran,” Jurnal Teknologi (Sciences & Engineering), vol. 78, no. 8-2, pp. 11–18, 2016.
[18] A. B. M. S. Sadi, T. Anam, M. Abdirazak, and F. Anan, “Applying ontological modeling on Quranic ”Nature” domain,” in 2016 7th Int. Conf. on Information and Communication Systems (ICICS), 2016, pp. 151–155.
[19] A. Ta’a, Q. A. Abed, and M. Ahmad, “Al-Quran ontology based on knowledge themes,” Journal of Fundamental and Applied Sciences, vol. 9, no. 5S, pp. 800–817, 2017.
[20] M. M. Alqahtani and E. Atwell, “Developing bilingual Arabic-English ontologies of Al-Quran,” in 2018 IEEE 2nd Int. Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), 2018, pp. 64–69.
[21] A. Daud, M. H. Ullah, A. R. Banjar, and A. A. Alshdadi, “Ontological modeling and semantic search in Quran,” International Journal of Computer Science and Network Security, vol. 22, no. 5, pp. 771–779, 2022.
[22] A. Ta’a et al., “Semantic approach for Al-Quran knowledge representa- tion in Islamic knowledge management framework,” Journal of Quranic Studies Research, vol. 6, no. 2, pp. 66–76, 2021.
[23] N. Basir et al., “Retrieval performance for USIM’s Quranic search engine,” Journal of Engineering and Applied Sciences, vol. 13, Special Issue 11, pp. 8894–8899, 2018.
[24] H. Z. Rad, S. Tiun, and S. Saad, “Lexical scoring system of lexical chain for Quranic document retrieval,” GEMA Online Journal of Language Studies, vol. 18, no. 2, pp. 59–78, 2018.
[25] F. S. Utomo, Y. Purwati, M. S. Azmi, L. Shafira, and A. Mudzakir, “Word embedding and imbalanced learning impact on Indonesian Quran ontology population,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 156–165, 2025.
[26] S. A. Pitchay and F. Ridzuan, “A systematic review analysis for Quran verses retrieval,” Journal of Engineering and Applied Sciences, vol. 11, Special Issue, pp. 629–634, 2016.
[27] S. A. Salloum, A. Q. AlHamad, M. Al-Emran, and K. Shaalan, “A survey of Arabic text mining,” in Intelligent natural language processing: Trends and applications, Springer, Cham, 2017, pp. 417–431.
[28] D. Suleiman, A. Awajan, and W. Al Etaiwi, “The use of Hidden Markov Model in natural Arabic language processing: A survey,” Procedia Computer Science, vol. 113, pp. 240–247, 2017.
[29] A. M. Azmi, A. O. Al-Qabbany, and A. Hussain, “Computational and natural language processing based studies of hadith literature: A survey,” Artificial Intelligence Review, vol. 52, no. 2, pp. 1369–1405, 2019.
[30] K. Darwish et al., “A panoramic survey of natural language processing in the Arab world,” Communications of the ACM, vol. 64, no. 4, pp. 72–81, 2021.
[31] Q. Bsoul, R. Abdul Salam, J. Atwan, and M. Jawarneh, “Arabic text clustering methods and suggested solutions for theme-based Quran clustering: Analysis of literature,” Journal of Information Science Theory and Practice, vol. 9, no. 4, pp. 15–38, 2021.
[32] S. K. Hamed and M. J. Ab Aziz, “A question answering system on Holy Quran translation based on question expansion technique and neural network classification,” Journal of Computer Science, vol. 12, no. 4, pp. 169–177, 2016.
[33] W. Antoun, F. Baly, and H. Hajj, “AraBERT: Transformer-based model for Arabic language understanding,” in Proc. 4th Workshop on Open- Source Arabic Corpora and Processing Tools, 2020, pp. 9–15.
[34] M. ElKomy and A. M. Sarhan, “TCE at Qur’an QA 2022: Arabic language question answering over Holy Qur’an using a post-processed ensemble of BERT-based models,” in Proc. 5th Workshop on Open- Source Arabic Corpora and Processing Tools, 2022, pp. 154–161.
[35] D. Premasiri, T. Ranasinghe, W. Zaghouani, and R. Mitkov, “DTW at Qur’an QA 2022: Utilising transfer learning with transformers for question answering in a low-resource domain,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 88–95.
[36] E. Aftab and M. K. Malik, “eRock at Qur’an QA 2022: Contemporary deep neural networks for Qur’an based reading comprehension question answers,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 96–103.
[37] B. Ahmed, M. Saad, and E. A. Refaee, “QQATeam at Qur’an QA 2022: Fine-tunning Arabic QA models for Qur’an QA task,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 130–135.
[38] A. Alsaleh et al., “LK2022 at Qur’an QA 2022: Simple transformers model for finding answers to questions from Qur’an,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 120–125.
[39] A. Keleg and W. Magdy, “SMASH at Qur’an QA 2022: Creating better faithful data splits for low-resourced question answering scenarios,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 136–145.
[40] Y. Mellah et al., “LARSA22 at Qur’an QA 2022: Text-to-text trans- former for finding answers to questions from Qur’an,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 112–119.
[41] A. Mostafa and O. Mohamed, “GOF at Qur’an QA 2022: Towards an efficient question answering for the Holy Qu’ran in the Arabic language using deep learning-based approach,” in Proc. 5th Workshop on Open- Source Arabic Corpora and Processing Tools, 2022, pp. 104–111.
[42] N. Singh, “niksss at Qur’an QA 2022: A heavily optimized BERT based model for answering questions from the Holy Qu’ran,” in Proc. 5th Workshop on Open-Source Arabic Corpora and Processing Tools, 2022, pp. 126–129.
[43] A. Sleem et al., “Stars at Qur’an QA 2022: Building automatic extractive question answering systems for the Holy Qur’an with transformer models and releasing a new dataset,” in Proc. 5th Workshop on Open- Source Arabic Corpora and Processing Tools, 2022, pp. 146–153.
[44] S. Alnefaie, A. Alsaleh, E. Atwell, M. Alsalka, and A. Altahhan, “LKAU23 at Qur’an QA 2023: Using transformer models for retrieving passages and finding answers to questions from the Qur’an,” in Proc. ArabicNLP 2023, 2023, pp. 720–727.
[45] M. A. Ateeq, S. Tiun, H. Abdelhaq, and N. Rahhal, “Arabic narrative question answering (QA) using transformer models,” IEEE Access, vol. 12, pp. 2760–2777, 2023.
[46] M. R. Rizqullah, A. Purwarianti, and A. F. Aji, “QASiNa: Religious domain question answering using Sirah Nabawiyah,” in 2023 10th Int. Conf. on Advanced Informatics: Concept, Theory and Application (ICAICTA), 2023, pp. 1–6.
[47] Z. Touati-Hamad, M. R. Laouar, I. Bendib, and S. Hakak, “Arabic Quran verses authentication using deep learning and word embeddings,” The International Arab Journal of Information Technology, vol. 19, no. 4, pp. 681–688, 2022.
[48] M. H. K. Alshammeri, “Deep learning and distributional semantics for the Qur’an,” Ph.D. dissertation, Univ. of Leeds, 2022.
[49] Z. Saadaoui, G. Tlig, and F. Jarray, “LLMs based approach for Quranic question answering,” in Proc. 20th Int. Conf. on Web Information Systems and Technologies (WEBIST 2024), 2024, pp. 397–404.
[50] S. Alfadhli, H. Alharbi, and A. Cherif, “qArI: A hybrid CTC/attention- based model for Quran recitation recognition using bidirectional LSTMP in an end-to-end architecture,” IEEE Access, vol. 12, pp. 90274–90289, 2024.
[51] A. Zafar, M. Wasim, S. Zulfiqar, T. Waheed, and U. Raza, “Transformer- based topic modeling for Urdu translations of the Holy Quran,” ACM Transactions on Asian and Low-Resource Language Information Pro- cessing, vol. 23, no. 9, Art. 196, 2024.
[52] A. Abbasi, M. J. Awan, and M. Ismail, “A deep learning approach to semantic clarity in Urdu translations of the Holy Quran,” Linguistica Antverpiensia, 2025, Art. no. 389676054.
[53] M. A. A. Danish Khaleeq, M. Tariq, and J. Iqbal, “Machine translation of Quranic verses: A transformer-based approach to Urdu rendering,” International Journal of Islamic Studies and Technology, 2025.
[54] K. Gaanoun and M. Alsuhaibani, “Sentiment preservation in Quran translation with artificial intelligence approach: Study in reputable English translation of the Quran,” Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1–15, 2025.
[55] M. Tariq, M. A. Awan, and D. Khaleeq, “Developing a Quranic QA system: Bridging linguistic gaps in Urdu translation using NLP and transformer model,” Linguistica Antverpiensia, 2025.
[56] Y. Aftab, M. A. Awan, D. Khaleeq, and T. Ismail, “Enhancing Quranic ethics and morality: An NLP-based semantic search model for Urdu translation,” International Journal of Islamic Studies and Technology, 2025.
[57] U. Shahid, M. Z. Hussain, and W. Sayers, “Computational analysis of Quran text using machine learning and large language models,” in 2025 8th Int. Conf. on Data Science and Machine Learning Applications (CDMA), 2025, pp. 18–24.
[58] A. B. Kamran, N. A. Butt, and A. Basharat, “Semantic enrichment of Hadith corpus: Knowledge graph generation from Islamic text,” Semantic Web Journal, vol. 15, no. 4, pp. 1247–1276, 2024.
[59] S. Aftar, A. El Ganadi, L. Gagliardelli, F. Ruozzi, and S. Bergamaschi, “RoBERT2VecTM: A novel approach for topic extraction in Islamic studies,” in Findings of the Association for Computational Linguistics: EMNLP 2024, 2024, pp. 9112–9123.
[60] M. Asgari-Bidhendi, M. A. Ghaseminia, and A. Rezaei, “Rezwan: Lever- aging large language models for comprehensive hadith text processing: A 1.2 M corpus development,” arXiv preprint arXiv:2510.03781, 2025.
[61] A. Omayrah, S. Alkhereyf, A. Abdelali, and H. Hassan, “HUMAIN at IslamicEval 2025 shared task 1: A three-stage LLM-based pipeline for detecting and correcting hallucinations in Quran and Hadith,” in Proc. IslamicEval @ ArabicNLP 2025, 2025.
[62] A. Al Adel, A. B. Soliman, M. S. Sawan, R. Al-Najjar, and A. Ashraf, “BurhanAI at IslamicEval 2025 shared task: Combating hallucinations in LLMs for Islamic content; Evaluation, correction, and retrieval-based solution,” in Proc. IslamicEval @ ArabicNLP 2025, 2025.
[63] A. Salah, M. Mahdi, and A. A. Badawy, “Exploring thematic structures in Surah Al-Baqarah using TF-IDF, dimensionality reduction, and K- means clustering,” Journal of Computing and Communication Systems, vol. 1, no. 1, pp. 15–28, 2025.
[64] A. N. An, F. Q. A. Tamami, Z. Daud, and N. M. Salleh, “Understanding the integration of deep learning and artificial intelligence in Quranic education and research through bibliometric analysis,” Educational Process: International Journal, vol. 14, no. 1, pp. 45–62, 2025.
[65] T. Jurczyk, R. Seidel, A. Bernhard, T. Scheffler, and A. El-Sayed, “Text mining Tafsir: Compilation and preliminary explorations of a curated corpus of 80 Qur’anic commentaries,” Journal of Digital Islamic Research, vol. 3, no. 1, pp. 97–125, 2025.
[66] K. A. Alam et al., “Automated authentication of Quranic verses using BERT (Bidirectional Encoder Representations from Transformers) based language models,” in Proc. New Horizons in Computational Linguistics for Religious Texts (Coling-Rel), 2025, pp. 59–66.
[67] T. M. H. Asda, T. S. Gunawan, M. Kartiwi, and H. Mansor, “Devel- opment of Quran reciter identification system using MFCC and neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 1, no. 1, pp. 168–175, 2016.
[68] A. A. Q. Al-Bakeri and A. A. Basuhail, “ASR for Tajweed rules: Integrated with self-learning environments,” International Journal of Information Engineering and Electronic Business, vol. 9, no. 6, pp. 1–9, 2017.
[69] F. Ahmad, S. Z. Yahya, and Z. Saad, “Tajweed classification using artificial neural network,” in 2018 Int. Conf. on Computational Approach in Smart Systems Design and Applications (ICASSDA), 2018, pp. 1–6.
[70] M. Al-Ayyoub, N. A. Damer, and I. Hmeidi, “Using deep learning for automatically determining correct application of basic Quranic recitation rules,” The International Arab Journal of Information Technology, vol. 15, no. 3A, pp. 620–625, 2018.
[71] A. M. Alagrami and M. M. Eljazzar, “Smartajweed: Automatic recognition of Arabic Quranic recitation rules,” arXiv preprint arXiv:2101.04200, 2020.
[72] K. M. Nahar, R. M. Al-Khatib, M. A. Al-Shannaq, and M. M. Barhoush, “An efficient Holy Quran recitation recognizer based on SVM learning model,” Jordanian Journal of Computers and Information Technology, vol. 6, no. 4, pp. 392–406, 2020.
[73] J. H. Alkhateeb, “A machine learning approach for recognizing the Holy Quran reciter,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, pp. 268–271, 2020.
[74] A. A. Harere and K. A. Jallad, “Mispronunciation detection of basic Quranic recitation rules using deep learning,” arXiv preprint arXiv:2305.06429, 2023.
[75] A. A. Harere and K. A. Jallad, “Quran recitation recognition using end- to-end deep learning,” arXiv preprint arXiv:2305.07034, 2023.
[76] N. Nigar, A. Wajid, S. A. Ajagbe, and A. Al-omari, “An intelligent framework based on deep learning for online Quran learning during pandemic,” Applied Computational Intelligence and Soft Computing, vol. 2023, Art. no. 5541699, 2023.
[77] S. Al-Fadhli, H. Al-Harbi, and A. Cherif, “Speech recognition models for Holy Quran recitation based on modern approaches and Tajweed rules: A comprehensive overview,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 12, pp. 793–807, 2023.
[78] M. A. Shakeel, H. A. Khattak, and M. Al-Hawawreh, “Deep acoustic modelling for Quranic recitation: Current solutions and future direc- tions,” IPSI BgD Transactions on Internet Research, vol. 20, no. 1, pp. 45–58, 2024.
[79] H. M. Osman, B. S. Mustafa, and B. M. Mahmood, “A deep learning approach for recognizing the noon rule for reciting Holy Quran,” Protek: Jurnal Ilmiah Teknik Elektro, vol. 11, no. 3, pp. 198–205, 2024.
[80] D. Shaiakhmetov, G. Gimaletdinova, K. Momunov, and A. Rakhimzhanova, “Evaluation of the pronunciation of Tajweed rules based on DNN as a step towards interactive recitation learning,” arXiv preprint arXiv:2503.23470, 2025.
[81] E. M. Alsukhni, M. N. Al-Kabi, and I. M. Alsmadi, “An automatic evaluation for online machine translation: Holy Quran case study,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 6, pp. 118–125, 2016.
[82] M. A. Hamdelsayed and E. Atwell, “Islamic applications of automatic question-answering,” SUST Journal of Engineering and Computer Sci- ence (JECS), vol. 17, no. 2, pp. 51–57, 2016.
[83] M. A. Saloot et al., “Hadith data mining and classification: A compara- tive analysis,” Artificial Intelligence Review, vol. 46, no. 1, pp. 113–132, 2016.
[84] S. J. Putra et al., “A semantic-based question answering system for Indonesian translation of Quran,” in Proc. 18th Int. Conf. on Asian Language Processing (IALP), 2016, pp. 126–131.
[85] A. Alosaimy and E. Atwell, “Sunnah Arabic Corpus: Design and methodology,” in Proc. 5th Int. Conf. on Islamic Applications in Com- puter Science and Technologies (IMAN 2017), 2017.
[86] S. Bougrine, H. Cherroun, and A. Abdelali, “Altruistic crowdsourcing for Arabic speech corpus annotation,” Procedia Computer Science, vol. 113, pp. 276–283, 2017.
[87] A. Mahmood, H. U. Khan, and W. Khan, “Query based information retrieval and knowledge extraction using Hadith datasets,” in 2017 13th Int. Conf. on Emerging Technologies (ICET), 2017, pp. 1–6.
[88] M. Alhawarat and M. Hegazi, “Revisiting k-means and topic modeling, a comparison study to cluster Arabic documents,” IEEE Access, vol. 6, pp. 42740–42751, 2018.
[89] A. Adeleke, N. Samsudin, A. Mustapha, and S. Ahmad Khalid, “Au- tomating Quranic verses labeling using machine learning approach,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 2, pp. 1089–1097, 2019.
[90] E. Atwell, “Using the web to model modern and Qur’anic Arabic,” in Arabic corpus linguistics, T. McEnery, A. Hardie, and N. Younis, Eds. Edinburgh: Edinburgh University Press, 2019, pp. 100–119.
[91] S. N. Olimat, “Euphemism in the Qur’an: A corpus-based linguistic approach,” International Journal of Computational Linguistics (IJCL), vol. 10, no. 2, pp. 16–32, 2019.
[92] J. Olsson, “Computing Qur’ans: A suggestion for a digital humanities approach to the question of interrelations between English Qur’an translations,” Journal of Qur’anic Studies, vol. 21, no. 2, pp. 83–115, 2019.
[93] N. Varghese and M. Punithavalli, “Lexical and semantic analysis of sacred texts using machine learning and natural language processing,” International Journal of Scientific & Technology Research, vol. 8, no. 12, pp. 2820–2827, 2019.
[94] H. J. A. Alshahrani, “The semantic prosody of natural phenomena in the Qur’an: A corpus-based study,” Ph.D. dissertation, Univ. of Leeds, 2020.
[95] I. Tlemsani, F. Marir, and M. Majdalawieh, “Screening of Murabaha business process through Quran and hadith: A text mining analysis,” Journal of Islamic Accounting and Business Research, vol. 11, no. 9, pp. 1909–1933, 2020.
[96] Z. Touati-Hamad, M. R. Laouar, and I. Bendib, “Quran content represen- tation in NLP,” in Proc. 10th Int. Conf. on Information Communication and Management, 2020, pp. 42–46.
[97] F. S. Utomo, N. Suryana, and M. S. Azmi, “Question answering systems on holy Quran: A review of existing frameworks, approaches, algorithms and research issues,” in Journal of Physics: Conf. Series, vol. 1501, no. 1, 2020, Art. no. 012022.
[98] M. L. Arifianto, “Utilizing the Quranic Arabic Corpus as a supplemen- tary teaching and learning material for Arabic syntax: An overview of a web-based Arabic linguistics corpus,” in Int. Seminar on Language, Education, and Culture (ISOLEC), 2021, pp. 403–412.
[99] H. Maraoui, K. Haddar, and L. Romary, “Arabic factoid Question- Answering system for Islamic sciences using normalized corpora,” Procedia Computer Science, vol. 189, pp. 164–172, 2021.
[100] D. I. A. Putra and M. Yusuf, “Proposing machine learning of Tafsir al-Quran: In search of objectivity with semantic analysis and Natural Language Processing,” in IOP Conf. Series: Materials Science and Engineering, vol. 1098, no. 2, 2021, Art. no. 022101.
[101] O. Ahmed, “Semantic search engine for the holy Quran,” Master’s thesis, Metropolia Univ. of Applied Sciences, 2024.
[102] M. A. Alqarni, “Embedding search for Quranic texts based on large language models,” The International Arab Journal of Information Tech- nology, vol. 21, no. 2, pp. 243–257, 2024.
[103] A. S. M. Khidhir, “An AI model for parsing the text of Holy Quran sentences,” Mesopotamian Journal of Quran Studies, pp. 20–25, 2024.
[104] F. Qamar, S. Latif, and R. Latif, “A benchmark dataset with larger context for non-factoid question answering over Islamic text,” arXiv preprint arXiv:2409.09844, 2024.
[105] R. Salah, M. Mukred, and L. Q. Zakaria, “A machine learning approach for named entity recognition in Classical Arabic natural language processing,” KSII Transactions on Internet and Information Systems, vol. 18, no. 11, pp. 3156–3178, 2024.
[106] A. Badawy, A. Salah, and M. Mahdy, “Digital Quran computing algorithms and applications,” International Journal of Computers and Informatics, vol. 7, pp. 79–90, 2025.
[107] A. Bengueddach, “Multi-dimensional NLP analysis of the Quran: Structure, semantics, and emotion across Qira¯’a¯t,” QURANICA: Inter- national Journal of Quranic Research, vol. 17, no. 1, pp. 32–52, 2025.
[108] E. Elrefai, T. Khaled, and A. Soliman, “ThinkDrill at IslamicEval 2025 shared task: LLM hybrid approach for Qur’an and Hadith question answering,” in Proc. IslamicEval @ ArabicNLP 2025, 2025.
[109] S. Lagrini, A. Debbah, and M. Redjimi, “A new semantic search approach for the Holy Quran based on discourse analysis and advanced word representation models,” International Journal of Computing and Digital Systems, 2025.
[110] I. Oshallah, M. Basem, A. Hamdi, and M. Khalil, “Cross-language approach for Quranic QA,” in Int. Congress on Information and Com- munication Technology, 2025, pp. 423–431.
[111] S. Saeed, S. Haider, and Q. Rajput, “On finding similar verses from the Holy Quran using word embeddings,” in 2020 Int. Conf. on Information Networking (ICOIN), 2020, pp. 524–528.
[112] S. Amin, R. Aly, Y. Allam, Y. Eid, and E. H. Mohamed, “NUR at IslamicEval 2025 shared task: Retrieval-augmented LLMs for Qur’an and Hadith QA,” in Proc. IslamicEval @ ArabicNLP 2025, 2025.
[113] B. Hamoud and E. Atwell, “Evaluation corpus for restricted-domain question-answering systems for the Holy Quran,” International Journal of Science and Research, vol. 6, no. 8, pp. 1924–1930, 2017.
[114] M. M. A. Alqahtani, “Quranic Arabic semantic search model based on ontology of concepts,” Ph.D. dissertation, Univ. of Leeds, 2019.
[115] W. A. Nashir et al., “A complete, multi-layered quranic treebank dataset with hybrid syntactic annotations for classical arabic processing,” Data in Brief, vol. 62, Art. no. 111940, 2025.
[116] A. O. Adeleke, N. A. Samsudin, A. Mustapha, and M. T. Mustapha, “Comparative analysis of text classification algorithms for automated labelling of Quranic verses,” International Journal of Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1419–1427, 2017.
[117] W. A. Nashir et al., “Noor: A pipeline framework for Classical Arabic parsing,” IEEE Access, vol. 13, pp. 167538–167557, 2025.
[118] B. B. Al-Onazi, W. A. Nashir, and A. A. Al-Shargabi, ““Diwan”: Constructing the Largest Annotated Corpus for Arabic Poetry,” IEEE Access, vol. 13, pp. 58927–58941, 2025.
[119] A. Basharat, A. B. Kamran, and M. Rehman, “SemanticTafsir: Building a cultural heritage ontology and knowledge graph from the Quranic exegesis of al-Tabari,” Semantic Web Journal, vol. 15, no. 6, pp. 1821–1854, 2024.
[120] R. G. Al-Anazi, W. A. Nashir, B. B. Al-Onazi, A. A. Alhamad, and A. A. Al-Shargabi, “Maqasid: A Hybrid CNN-BiLSTM Framework for Nuanced Thematic Classification of Arabic Poetry,” IEEE Access, vol. 13, pp. 189421–189443, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The copyright is transferred to the University of Science and Technology, Sana’a, Yemen.
This work is licensed under a Creative Commons Attribution 4.0 International License.
