Abstract
The text of the Qur’an has been analysed, segmented and annotated by linguists and religious scholars, using a range of representations and formats, Quranic resources in different scopes and formats can be difficult to link due to their complexity. Qur’an segmentation and annotation can be represented in a heterogeneous structure (e.g., CSV, JSON, and XML). However, there is the lack of a standardised mapping formalisation for the data. For this reason, this study’s motivation is to link morphological segmentation tags and syntactic analyses, in Arabic and Buckwalter forms, to the Hakkoum ontology to enable further clarification of the Qur’an. For achieving this aim, the paper combines two mapping methods: the RDF (resources description framework) mapping language, which is an R2RML extension (the W3C level necessary when mapping relational databases into RDF), and Cellfie plugin, which is a part of the Protégé system. The proposed approach provides the possibility to automatically map and merge the heterogeneous data sources into an RDF data model. Also, the integrated ontology is evaluated by a SPARQL query using an Apache Jena Fuseki server. This experiment was conducted in all the Qur’an chapters and verses, containing all the words and segments of the entire Qur’an corpus.
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Notes
- 1.
Qur’an ontology Data can be downloaded via: http://Quranontology.com/.
- 2.
This is a computer-readable orthographic transliteration technique that uses ASCII characters to represent Arabic text for non-Arabic academics.
- 3.
It is can be accessed via: http://textminingthequran.com/.
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Acknowledgements
Ibtisam Alshammari would like to express her deepest gratitude to Saudi Arabia Cultural Bureau (SACB) in London, United Kingdom, as well as University of Leeds, and University of Hafr Al-Batin.
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Alshammari, I.K., Atwell, E., Alsalka, M.A. (2022). Automatic Mapping of Quranic Ontologies Using RML and Cellfie Plugin. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_28
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