Artificial Intelligence for Islamic Ontology Linking Qur’an and Hadith Resources

The primary objective of this thesis is to integrate various Islamic resources, specifically the Qur’an and Hadith, building upon existing academic research. Although previous works have produced valuable contributions, they remain uncoordinated and heterogeneous, raising significant challenges for interoperability and integration. This thesis addresses these limitations by unifying these inconsistent resources into a coherent and comprehensive Islamic ontology, ensuring a more accurate and structured representation of Islamic knowledge and facilitating wider computational applications.

The project employs a multi-stage methodology. A workflow combining RML, Cellfie plugin, and SDM-RDFizer interpreter is utilised to integrate Qur’anic resources, enabling data transformation and alignment. This framework successfully maps the data correctly, demonstrating the effectiveness of the Cellfie plugin tool. For the Hadith corpus, knowledge extraction approaches are applied to the LK-Hadith corpus. First experiment with Arabic named entity recognition (ANER) using a limited number of BERT-based Arabic pretrained language models (PLMs) employed on the Sahih Albukhari book produces insufficient results for semantic integration, leading to the adoption of topic modelling methods. The topic modelling and extraction involves Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), BERTopic with Arabic PLMs, and GPT-4. This experiment presented a bilingual Hadith_Teaching_Topics (HTT) dataset. Then, the semantic relationships between Qur’anic and Hadith topics are evaluated through the Quran_Hadith_Dataset (QH_Dataset) using multiple large language models (LLMs), enriching the construction of the Related Quran-Hadith Topics (RQHT) ontology. The ontology is subsequently extended to incorporate the QuranOntology, the entire six Hadith books derived from the LK-Hadith corpus, and their derived topics based on the HTT dataset.

This thesis advances the field by establishing a methodological framework for integrating heterogeneous Islamic textual resources by involving multiple data transformations, knowledge extraction, and ontology engineering techniques. Within this framework, the Qur’anic resources are integrated to enhance the unification process, the bilingual HTT dataset is developed to augment the low-resource Arabic annotation and support the final integrated ontology, and the RQHT ontology is introduced to formally represent the semantic relationships between Qur’an and Hadith topics. Collectively, these contributions boost the unification process of Qur’an and Hadith knowledge within a comprehensive and logically consistent semantic Islamic ontology.

Evaluation through quantitative and qualitative measures determines the Islamic ontology’s logical consistency, representational completeness, and suitability for advanced computational applications. This research provides a scalable semantic framework to support more knowledge representation and reasoning across core Islamic texts.