Alrehaili, SM orcid.org/0000-0002-4957-2478, Alqahtani, M orcid.org/0000-0001-9403-1286 and Atwell, E orcid.org/0000-0001-9395-3764 (2018) A Hybrid Method of Aligning Arabic Qur’anic Semantic Resources. 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. 108-113.
Abstract
Ontology alignment is a necessary step for enabling interoperability between ontology entities and for avoiding redundancy and variation that may occur when integrating them. The automation of bilingual ontology alignment is challenging due to the variation an entity can be expressed in, in different ontologies and languages. The goal of this paper is to compare various ontology alignment methods for matching ontological bilingual Qur’anic resources and to go beyond them, which is achieved via a new hybrid alignment method. The new method consists of aggregating multiple similarity measures for a given pair of concepts into a single value, taking advantage of combining fuzzy bilingual lexical and structure-based methods for improving the performance of automatic ontology alignment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in Proceedings of ASAR'2018 Arabic Script Analysis and Recognition. Uploaded in accordance with the publisher's self-archiving policy. 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: | alignment, Quran, ontology |
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 10:38 |
Last Modified: | 29 Mar 2018 12:56 |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128592 |