Convergence of Artificial Intelligence, the Semantic Web, and Large Language Models in Knowledge Management Systems for Islamic Texts: A Critical Review from a System Engineering Perspective

Document Type : Original Article

Author

Information and Knowledge Dissemination, Islamic Information and Documents Management Research Center, Qom, Iran

Abstract

Knowledge management of Islamic texts has long faced challenges in organizing, retrieving, and analyzing large volumes of complex textual data. The emergence and maturation of artificial intelligence technologies, particularly the semantic web, knowledge graphs, and large language models, have opened new horizons in this field. This article provides an analytical and critical review of the convergence of these three technological streams in knowledge management systems for Islamic texts over the period 2023–2026. The primary objective is not only to identify and categorize recent advances but also to conduct an in-depth engineering assessment of these developments in terms of system architecture, scalability, semantic accuracy, reliability, and compliance with domain requirements. Through a systematic analysis of the research literature—comprising more than 70 selected articles from an initial title of 200 publications—a novel conceptual framework is proposed for analyzing intelligent knowledge management systems for Islamic texts, incorporating technical, methodological, and domain-specific considerations. The findings indicate that, while technologies such as semantic knowledge graphs and large language models have demonstrated considerable capabilities in organizing, retrieving, and generating knowledge from Islamic texts, fundamental challenges—including hallucinations in language models, fragmented ontologies, the scarcity of high-quality annotated data, the complexities of source validation, and the need for model interpretability—remain major barriers to achieving fully reliable and high-performance systems. By proposing a six-dimensional research roadmap, the article identifies future directions for developing intelligent knowledge management systems for Islamic texts that can respond effectively and responsibly to the needs of Islamic textual analysis and processing.

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References

Alshammari, I. K. , Atwell, E. , & Alsalka, M. A. (2024). Linking Quran and Hadith topics in an ontology using word embeddings and Cellfie plugin. Proceedings of the 7th International Conference on Natural Language and Speech Processing, 449-455.
ArabicNLP Shared Task. (2025). Retrieval-augmented LLMs for Qur'anic and Hadith content verification. Proceedings of ArabicNLP 2025.
Bashir, M. H. , et al. (2023). Arabic natural language processing for Qur'anic research: A systematic review. Artificial Intelligence Review, 56(7), 6801-6854.
Bendjamaa, F. , & Taleb, N. (2024). OntoDin: An Islamic ontology of Quran and Hadith. The International Arab Journal of Information Technology, 21(5), 773-784.
Berners-Lee, T., Hendler, J. , & Lassila, O. (2001). The Semantic Web. Scientific American, 284(5), 34-43.
Brown, T. B. , Mann, B. , Ryder, N. , Subbiah, M. , Kaplan, J. , Dhariwal, P. , . . . & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on Artificial Intelligence. https://digital-strategy. ec. europa. eu/en/library/ethics-guidelines-trustworthy-ai
Feng, X. , Wu, X. , & Meng, H. (2024). Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema. In CEUR Workshop Proceedings (Vol. 3841, pp. 117-135). (CEUR Workshop Proceedings). https://ceur-ws. org/Vol-3841/Paper19. pdf.
Haji Esmaeili, M. , & Haji Molla Mirzaee, H. (2021). Presenting a model of knowledge management in the Islamic value system and comparatively comparing it with other common models. Strategic Management of Organizational Knowledge, 3(4), 71-100. doi: 10. 47176/smok. 2020. 1228.
Harrag, Fouzi & Al-Nasser, Abdullah & Al-Musnad, Abdullah & Al-Shaya, Rayan. (2020). Quran Intelligent Ontology Construction Approach Using Association Rules Mining. 10. 48550/arXiv. 2008. 03232.
Hogan, A. , Blomqvist, E. , Cochez, M. , D’amato, C. , De Melo, G. , Gutierrez, C. , Kirrane, S. , Gayo, J. E. L. , Navigli, R. , Neumaier, S. , Ngomo, A. -C. N. , Polleres, A. , Rashid, S. M. , Rula, A. , Schmelzeisen, L. , Sequeda, J. , Staab, S. , & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37. https://doi. org/10. 1145/3447772.
Ji, S. , Pan, S. , Cambria, E. , Marttinen, P. , & Yu, P. S. (2022). A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494-514. https://doi. org/10. 1109/TNNLS. 2021. 3070843.
Kamran, A. B. , Abro, B. , & Basharat, A. (2023). SemanticHadith: An ontology-driven knowledge graph for the hadith corpus. Journal of Web Semantics, 78, 100797.
Kamran, A. B. , Butt, N. A. , & Basharat, A. (2026). Semantic enrichment of Hadith corpus—Knowledge graph generation from Islamic text. Journal of Al-Quds Open University for Research and Studies. Advance online publication. https://doi. org/10. 1177/22104968261431425.
Khan, H. U. , Saqlain, S. M. , Shoaib, M. , & Sher, M. (2013). Ontology based semantic search in Holy Quran. International Journal of Future Computer and Communications, 2(6), 570–575. https://doi. org/10. 7763/IJFCC. 2013. V2. 229.
Kommineni, V. K. , König-Ries, B. , & Samuel, S. (2024). From human experts to machines: An LLM supported approach to ontology and knowledge graph construction [Preprint]. arXiv. https://doi. org/10. 48550/arXiv. 2403. 08345.
Malhas, R. , Mansour, W. , & Elsayed, T. (2023). Qur'an QA 2023 shared task: Overview of passage retrieval and reading comprehension tasks over the Holy Qur'an. In H. Sawaf, S. El-Beltagy, W. Zaghouani, W. Magdy, A. Abdelali, N. Tomeh, I. Abu Farha, N. Habash, S. Khalifa, A. Keleg, H. Haddad, I. Zitouni, K. Mrini, & R. Almatham (Eds.), Proceedings of ArabicNLP 2023 (pp. 690–701). Association for Computational Linguistics. https://doi. org/10. 18653/v1/2023. arabicnlp-1. 76.
Mohammad, Y. , Nachouki, M. , & Mohamed, E. A. (2024). Knowledge management systems in business management using knowledge graphs and semantic technologies. International Journal of Knowledge Management, 21(1). https://doi. org/10. 4018/IJKM. 369121.
National Institute of Standards and Technology (NIST). (2023). Artificial intelligence risk management framework (AI RMF 1. 0). U. S. Department of Commerce. https://doi. org/10. 6028/NIST. AI. 100-1
OECD. (2019). OECD principles on artificial intelligence. Organisation for Economic Co-operation and Development. https://oecd. ai/en/ai-principles
Pan, J. Z. , Razniewski, S. , Kalo, J. -C. , Singhania, S. , Chen, J. , Dietze, S. , Jabeen, H. , Omeliyanenko, J. , Zhang, W. , Lissandrini, M. , Biswas, R. , de Melo, G. , Bonifati, A. , Vakaj, E. , Dragoni, M. , & Graux, D. (2023). Large language models and knowledge graphs: Opportunities and challenges [Preprint]. arXiv. https://doi. org/10. 48550/arXiv. 2308. 06374.
Peng, H. , & Yang, W. (2024). Knowledge graph construction method for commercial aircraft fault diagnosis based on logic diagram model. Aerospace, 11(9), 773.
Qamar, F. , Latif, S. , & Latif, R. (2024). A benchmark dataset with larger context for non-factoid question answering over Islamic text [Preprint]. arXiv. https://doi. org/10. 48550/arXiv. 2409. 09844.
Ta'a, A. , ShaariaHj Ashari, M. S., Che Mohamed Arif, A. S. , Abu Bakar, M. S., & Mahmad, M. A. (2021). Semantic Approach for Al-Quran Knowledge Representation in Islamic Knowledge Management Framework. Journal of Quranic Sciences and Research, 2(1), 35-46.
Uriawan, Wisnu & Hamza, Aria & Nuralim, Ade & Purnama, Adi & Yunus, Ahmad & Putri, Anissya. (2025). Implementing a Sharia Chatbot as a Consultation Medium for Questions About Islam. 10. 48550/arXiv. 2512. 16644.
Xue, B. , & Zou, L. (2023). Knowledge graph quality management: A comprehensive survey. IEEE Transactions on Knowledge and Data Engineering, 35(5), 4969–4988. https://doi. org/10. 1109/TKDE. 2022. 3150080.
Islamic Knowledge Management
Volume 7, Issue 2 - Serial Number 14
October 2025
Pages 128-165

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History

  • Receive Date: 28 April 2025
  • Revise Date: 18 July 2025
  • Accept Date: 01 August 2025

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