A Comparative Evaluation of Ontology Learning Techniques in the Context of the Qur’an

Ontology Learning refers to the automatic or semi-automatic process of creating ontologies by extracting terms, concepts, and relationships from text written in natural languages. This process is essential, as manually building ontologies is time-consuming and labour-intensive. The Qur'an, a vast source of knowledge for Muslims, presents linguistic and cultural complexities, with many words carrying multiple meanings depending on context. Ontologies offer a structured way to represent this knowledge, linking concepts systematically. Although various ontologies have been developed from the Qur'an for purposes such as advanced querying and analysis, most rely on manual creation methods. Few studies have examined the use of Ontology Learning for Qur’anic ontologies. Thus, this study evaluates three Ontology Learning techniques: Named Entity Recognition (NER), statistical methods, and Quranic patterns. The NER aims to find names represented by entity, statistical techniques aimed at finding frequently occurring words, and pattern-based techniques aim to identify complex relationships and multi-word expressions. The Ontology Learning techniques were evaluated based on precision, recall, and F-measure to assess extraction accuracy. The NER technique achieved an average precision of 0.62, statistical methods of 0.45, and pattern-based techniques of 0.58, indicating the strengths and weaknesses of each approach for extracting relevant terms as concepts, instances, or relations. This indicates that improvements or enhancements to the existing techniques are necessary for more accurate results. Future work will focus on refining or adapting patterns based on the structure of the Qur'an translation using LLMs.