Abstract

Ontology learning (OL) Plays an important role in many fields such as semantic web, Data Integration and Interoperability, Machine Translation, and Information Retrieval. The success of any of these fields depends on the quality of its ontologies. This paper presents a comprehensive survey of research on the Ontology Learning for Arabic texts. Most of the previous works focused on three main issues: extracting the terms, extracting the semantic relations, and building the ontology from the Arabic text. There are more of techniques and methods can be used for this process. In this paper, first we present the Arabic challenges that were reasons for developing few Arabic Ontology Learning systems. Second we make a research comparison based on the techniques used and their results. Third, we pointed limitations and comments of research works on Arabic Ontology Learning. Finally, we concluded the paper and outlined our future research direction in this area.

Highlights

  • The World Wide Web is a huge repository of unstructured data

  • In this paper we overview the research that works on Ontology Learning for Arabic text and their results, we found that the research focused on Extracting the terms, Extracting the semantic relations and building the Ontology automatically

  • In This paper, we review these challenges and survey some of the recent research aiming to improve the Arabic Ontology Learning

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Summary

INTRODUCTION

The World Wide Web is a huge repository of unstructured data These data are difficult to process by humans and difficult to understand by machines. Despite the ontology importance in the semantic web, it is very important in more fields such as Data Integration and Interoperability, Machine Translation, and Information Retrieval. Due to its importance for the semantic web and other fields, the process of building ontologies is important. Extracting semantic relations is one of the most important phases in the Ontology learning process. In this paper we overview the research that works on Ontology Learning for Arabic text and their results, we found that the research focused on Extracting the terms, Extracting the semantic relations and building the Ontology automatically. GHEITH: An Overview of Ontology Learning Process from Arabic Text

Ontology
Ontology Learning
TERM EXTRACTION
Rule-Based NER
Machine Learning-Based NER
Hybrid Based NER
SEMANTIC RELATION EXTRACTION
AUTOMATIC ONTOLOGY CONSTRUCTION
12 Surahs from the Holy Quran related to the stories of prophets in the
COMMENTS AND LIMITATIONS OF RESEARCH WORK ON ARABIC OL
CONCLUSION AND FUTURE WORK

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