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A New Concept Extraction Method for Ontology Construction From Arabic Text
Abeer Alarfaj, Abdulmalik Alsalamn
Pages - 1 - 21     |    Revised - 31-12-2019     |    Published - 01-02-2020
Volume - 9   Issue - 1    |    Publication Date - February 2020  Table of Contents
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KEYWORDS
Ontology Construction, Arabic Ontology, Arabic Language Processing, Concept Extraction, Arabic Term Extraction, Specific Domain Corpus.
ABSTRACT
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. The Arabic language has complex morphological, grammatical, and semantic aspects. Due to complexity of Arabic language, automatic Arabic terminology extraction is difficult. In addition, concept extraction from Arabic documents has been challenging research area, because, as opposed to term extraction, concept extraction are more domain related and more selective. In this paper, we present a new concept extraction method for Arabic ontology construction, which is the part of our ontology construction framework. A new method to extract domain relevant single and multi-word concepts in the domain has been proposed, implemented and evaluated. Our method combines linguistic, statistical information and domain knowledge. It first uses linguistic patterns based on POS tags to extract concept candidates, and then stop words filter is implemented to filter unwanted strings. To determine relevance of these candidates within the domain, different statistical measures and new domain relevance measure are implemented for first time for Arabic language. To enhance the performance of concept extraction, a domain knowledge will be integrated into the module. The concepts scores are calculated according to their statistical values and domain knowledge values. In order to evaluate the performance of the method, precision scores were calculated. The results show the high effectiveness of the proposed approach to extract concepts for Arabic ontology construction.
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Dr. Abeer Alarfaj
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nora Bint AbdulRahman University - Saudi Arabia
aaalarfaj@pnu.edu.sa
Dr. Abdulmalik Alsalamn
Department of Computer Science, College of Computer and Information Sciences, King Saud University - Saudi Arabia


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