Framework of Semantic Annotation of Arabic Document using Deep Learning

Saeed Albukhitan, Ahmed Alnazer, Tarek Helmy*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Semantic Web vision is to have machines interpret and understand the content of Web documents. There is a need to convert the existing Web of documents into an understandable format, which could be done by automatic semantic annotation. Annotation could be performed using a set of tools provided with general and domain-specific ontologies. The aim of this paper is to present a generic semantic annotation framework of Arabic text using deep learning models. The framework produces annotations using different output formats for a given set of Arabic documents and ontologies. With a prototype of the framework, the initial evaluation shows a promising performance using different public Arabic word embedding models with different vectorization and matching techniques.

Original languageEnglish
Pages (from-to)989-994
Number of pages6
JournalProcedia Computer Science
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V.All rights reserved.


  • Arabic Language
  • Deep Learning
  • Ontology
  • Semantic Annotation

ASJC Scopus subject areas

  • General Computer Science


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