Semantic web annotation using deep learning with Arabic morphology

Saeed Albukhitan, Ahmed Alnazer, Tarek Helmy*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language.

Original languageEnglish
Pages (from-to)385-392
Number of pages8
JournalProcedia Computer Science
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 The Authors. Published by Elsevier B.V.


  • Arabic Language
  • Deep Learning
  • Ontology
  • Semantic Annotation

ASJC Scopus subject areas

  • General Computer Science


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