Arabic Ontology Learning from Un-structured Text

Saeed Albukhitan, Tarek Helmy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Ontology Learning (OL) from a text is a process that consists of text processing, knowledge extraction, and ontology construction. For Arabic language, text processing, and knowledge extraction tasks are not mature as for Latin languages. They have not been integrated into the full Arabic OL pipeline. Currently, there is very little automated support for using knowledge from Arabic literature in semantically-enabled systems. This paper demonstrates the feasibility of using some existing OL methods for Arabic text and elicits proposals for further work toward building open domain OL systems for Arabic. This is done by building an OL system based on some available NLP tools for Arabic text utilizing GATE text analysis system for corpus and annotation management. The prototype is evaluated similarly to other OL systems and its performance is promising and recommended to enable more effective research and application of Arabic ontology learning.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509044702
StatePublished - 12 Jan 2017

Publication series

NameProceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Arabic Language Processing
  • Arabic Ontology
  • Ontology learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications


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