Arabic Ontology Learning from Un-structured Text

Saeed Albukhitan; Tarek Helmy

doi:10.1109/WI.2016.0082

Arabic Ontology Learning from Un-structured Text

Saeed Albukhitan, Tarek Helmy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

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 language	English
Title of host publication	Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	492-496
Number of pages	5
ISBN (Electronic)	9781509044702
DOIs	https://doi.org/10.1109/WI.2016.0082
State	Published - 12 Jan 2017

Publication series

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

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Arabic Language Processing
Arabic Ontology
Ontology learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications

Access to Document

10.1109/WI.2016.0082

Cite this

Albukhitan, S., & Helmy, T. (2017). Arabic Ontology Learning from Un-structured Text. In Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 (pp. 492-496). Article 7817099 (Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WI.2016.0082

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Albukhitan, S & Helmy, T 2017, Arabic Ontology Learning from Un-structured Text. in Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016., 7817099, Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 492-496. https://doi.org/10.1109/WI.2016.0082

Arabic Ontology Learning from Un-structured Text. / Albukhitan, Saeed; Helmy, Tarek.
Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 492-496 7817099 (Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Arabic Ontology Learning from Un-structured Text

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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