Alqahtani, M orcid.org/0000-0001-9403-1286 and Atwell, E orcid.org/0000-0001-9395-3764 (2018) Developing Bilingual Arabic-English Ontologies of Al-Quran. In: Proceedings of ASAR'2018 Arabic Script Analysis and Recognition. ASAR'2018 Arabic Script Analysis and Recognition, 12-14 Mar 2018, Alan Turing Institute, The British Library, London UK. IEEE , pp. 96-101.
Abstract
The main aim of developing a Quranic ontology is to facilitate the retrieval of knowledge from Al-Quran. Additionally, Quranic ontologies will enrich the raw Arabic and English Quran text with Islamic semantic tags. However, current Quran ontologies have different: scopes, formats, and entity names for the same concepts. Additionally, a single Quranic ontology does not cover most of the knowledge in Al-Quran. Therefore, these ontologies need to be increased, normalised, aligned and combined with other Quran resources such as Quran chapter and verse names, Quran word meanings, and other Quranic datasets. This paper reviews current Quran ontologies and datasets. Then, it presents several stages for developing Arabic-English Quran ontologies from different datasets related to Al Quran.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Arabic ontology; semanitc taging; Al Quran |
Dates: |
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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: | 16 Mar 2018 11:01 |
Last Modified: | 25 Apr 2018 10:43 |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128590 |