Alshammari, I.K. orcid.org/0000-0002-7619-373X, Atwell, E. and Alsalka, M.A. (2024) Linking Quran and Hadith Topics in an Ontology using Word Embeddings and Cellfie Plugin. In: Abbas, M. and Freihat, A.A., (eds.) Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024). 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024), 19-20 Oct 2024, Trento, Italy. Association for Computational Linguistics , pp. 449-455.
Abstract
Qur’an and Hadith are the sacred texts of the Islamic religion. Arabic Qur’an and Hadith texts have been analyzed and annotated by re-searchers using a variety of domains, representations, and formats to improve the accessibility of Islamic knowledge. However, the many and diverse Islamic resources raise a potential challenge in linking and integrating them. The main objective of this work is to link Qur’an and Hadith topics and integrate them with related knowledge from different Islamic resources. The proposed methodology is to use a combination of word embeddings-based BERT with the Cellfie tool to achieve more accurate and meaningful data integration. The results of using the CL-AraBERT word embedding model display efficiency performance in F1 score and accuracy metrics with 91% and 84% respectively. At the same time, the constructed ontology, RQHT, links the Qur’an and Hadith topics with their related knowledge properly and consistently.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This is an open access conference paper under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Nov 2024 16:25 |
Last Modified: | 11 Nov 2024 16:25 |
Published Version: | https://aclanthology.org/2024.icnlsp-1.46 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219409 |