Developing an Arabic-Urdu Ontology of Quranic Concepts: A Semantic Approach

Fouzia Nadeem; Muhammad Arshad Awan; Danish Khaleeq; Muhammad Tariq

Authors

Fouzia Nadeem Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad
Dr. Muhammad Arshad Awan Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad. https://orcid.org/0000-0002-3351-9664
Danish Khaleeq Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad https://orcid.org/0009-0000-6961-4922
Muhammad tariq Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad https://orcid.org/0009-0000-9727-7484

Keywords:

Arabic-Urdu Ontology, Quranic Concepts, Semantic Mapping, Computational Linguistics, Knowledge Representation

Abstract

An Arabic-Urdu ontology system dedicated to Quranic concepts represents a necessity for protecting the semantic value and making religious texts more accessible during Quranic study. Ontology-driven annotation tools show their ability to achieve precise translations and thematic searches by establishing their effects on the translation process. Researchers built this ontology using Protégé 5.6.4 which classifies Quranic concepts into twelve specific sections from Corpus.quran.com: Artifact, Astronomical Body, Event, False Deity, Holy Book, Language, Living Creation, Location, Physical Attribute, Physical Substance, Religion and Weather Phenomena. Validation of the ontology included expert evaluation and a HermiT computational assessment that led to user testing and an accuracy rate of 89.31%. The system uses SPARQL queries as a method to achieve both organized and efficient retrieval of Quranic knowledge. The analysis emphasizes the value of ontological structures as a means to connect Arabic and Urdu semantics which then improves both Quranic interpretation and computational linguistic understanding. While the methodology effectively maps Quranic concepts, challenges such as language nuances and theological precision persist, requiring further advancements in machine learning and natural language processing. Future research should focus on expanding ontology categories, integrating AI-based models, and enhancing phonetic mappings to improve the ontology’s adaptability and usability in diverse linguistic and cultural settings.

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