Extraction of concept and concept relation for Islamic term using syntactic pattern approach
Ontology Learning is a semi automation step to learn ontology from text. The identification of a term become a prerequisite for all aspects of Ontology Learning. The Ontological Learning Layer is started by identifying terms, synonyms, concepts, hierarchical concepts, relationships and rules for var...
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Penerbit Universiti Kebangsaan Malaysia
2018
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my-ukm.journal.177662021-12-24T08:49:37Z http://journalarticle.ukm.my/17766/ Extraction of concept and concept relation for Islamic term using syntactic pattern approach Saidah Saad, Ummu Kalsom A. Latiff, Ontology Learning is a semi automation step to learn ontology from text. The identification of a term become a prerequisite for all aspects of Ontology Learning. The Ontological Learning Layer is started by identifying terms, synonyms, concepts, hierarchical concepts, relationships and rules for various domain text including Islamic-based text or Islamic Glossary. The glossary of Islamic terms translated into English has been in abundance and requires extraction of important information for a clear understanding of an Islamic term. The existence of a list of Islamic terms is to minimize the spelling diversity, to seek the concept of the term and provide guidance for a unique Islamic concept. However, this electronic form provides a serious problem to achieve machine operation. The study aims to identify and extract concepts, taxonomies, relationships and rules that can be built on the domain of the terms in Islamic glossary specific to the field of Islamic Pillars. This extraction involves the use of Hearst pattern approach. The data set used is from the Dictionary or Islamic Glossary of the International Islamic University of Malaysia (DEED 2015). The dictionary consists of Islamic terms, which are the concepts and intentions of each concepts in alphabetical order. The study used six phases involving the preparation, processing and testing phase that are merged with the methodology design of the study. A total of 41 concepts were successfully extracted based on 6 Hearst pattern, 31 manually generated rules from 19 sentences and 9 non-taxonomic relationships. The result of the study concluded that the objective of this study was achieved in the scope determined when the results of the study and testing conducted by the assessors in the domain showed positive results. Research constraints are presented to enable researchers to improve the research from time to time. Research proposals for future research have been described so that this study will be more useful and expanded to the next comprehensive guide of Muslims. Penerbit Universiti Kebangsaan Malaysia 2018-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17766/1/06.pdf Saidah Saad, and Ummu Kalsom A. Latiff, (2018) Extraction of concept and concept relation for Islamic term using syntactic pattern approach. Asia-Pacific Journal of Information Technology and Multimedia, 7 (2). pp. 71-84. ISSN 2289-2192 https://www.ukm.my/apjitm/articles-year.php |
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Ontology Learning is a semi automation step to learn ontology from text. The identification of a term become a prerequisite for all aspects of Ontology Learning. The Ontological Learning Layer is started by identifying terms, synonyms, concepts, hierarchical concepts, relationships and rules for various domain text including Islamic-based text or Islamic Glossary. The glossary of Islamic terms translated into English has been in abundance and requires extraction of important information for a clear understanding of an Islamic term. The existence of a list of Islamic terms is to minimize the spelling diversity, to seek the concept of the term and provide guidance for a unique Islamic concept. However, this electronic form provides a serious problem to achieve machine operation. The study aims to identify and extract concepts, taxonomies, relationships and rules that can be built on the domain of the terms in Islamic glossary specific to the field of Islamic Pillars. This extraction involves the use of Hearst pattern approach. The data set used is from the Dictionary or Islamic Glossary of the International Islamic University of Malaysia (DEED 2015). The dictionary consists of Islamic terms, which are the concepts and intentions of each concepts in alphabetical order. The study used six phases involving the preparation, processing and testing phase that are merged with the methodology design of the study. A total of 41 concepts were successfully extracted based on 6 Hearst pattern, 31 manually generated rules from 19 sentences and 9 non-taxonomic relationships. The result of the study concluded that the objective of this study was achieved in the scope determined when the results of the study and testing conducted by the assessors in the domain showed positive results. Research constraints are presented to enable researchers to improve the research from time to time. Research proposals for future research have been described so that this study will be more useful and expanded to the next comprehensive guide of Muslims. |
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Article |
author |
Saidah Saad, Ummu Kalsom A. Latiff, |
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Saidah Saad, Ummu Kalsom A. Latiff, Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
author_facet |
Saidah Saad, Ummu Kalsom A. Latiff, |
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Saidah Saad, |
title |
Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
title_short |
Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
title_full |
Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
title_fullStr |
Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
title_full_unstemmed |
Extraction of concept and concept relation for Islamic term using syntactic pattern approach |
title_sort |
extraction of concept and concept relation for islamic term using syntactic pattern approach |
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Penerbit Universiti Kebangsaan Malaysia |
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2018 |
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http://journalarticle.ukm.my/17766/1/06.pdf http://journalarticle.ukm.my/17766/ https://www.ukm.my/apjitm/articles-year.php |
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13.214268 |