Arabic ontology learning using deep learning

Saeed Albukhitan, Tarek Helmy, Ahmed Alnazer

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

18 Scopus citations

Abstract

Ontology, the backbone of Semantic Web, is defined as the formal specification of conceptual hierarchy with relationships between concepts. Ontology Learning (OL) is a process to create an ontology from text automatically or semi-Automatically. OL is an important topic in the Semantic Web field in the last two decades but it is still not mature in Arabic not like Latin languages. Currently, there is a limited support for using knowledge from Arabic literature automatically in semanticallyenabled systems. Deep Learning (DL), an artificial neural networks learning based application, has proved a good improvement in multiple areas including text mining. By using DL, it is possible to have word embedding as distributed word representations from textual data. The application of DL to aid Arabic ontology development remains largely unexplored. This paper investigates the performance of implementing DL with Arabic ontology learning tasks using major models such as Continuous Bag of Words (CBOW) and Skip-gram. Initial performance results are promising as an effective application of Arabic ontology learning.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
PublisherAssociation for Computing Machinery, Inc
Pages1138-1142
Number of pages5
ISBN (Electronic)9781450349512
DOIs
StatePublished - 23 Aug 2017

Publication series

NameProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017

Bibliographical note

Publisher Copyright:
© 2017 ACM.

Keywords

  • Arabic ontology
  • Deep learning
  • Ontology learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications

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