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Learning Non-Taxonomic Relations of Ontologies: A Systematic Review

Learning Non-Taxonomic Relations of Ontologies: A Systematic Review

Mohamed Hassan Mohamed Ali, Said Fathalla, Mohamed Kholief, Yasser Fouad Hassan
Copyright: © 2021 |Volume: 17 |Issue: 1 |Pages: 26
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799859703|DOI: 10.4018/IJSWIS.2021010105
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MLA

Ali, Mohamed Hassan Mohamed, et al. "Learning Non-Taxonomic Relations of Ontologies: A Systematic Review." IJSWIS vol.17, no.1 2021: pp.97-122. http://doi.org/10.4018/IJSWIS.2021010105

APA

Ali, M. H., Fathalla, S., Kholief, M., & Hassan, Y. F. (2021). Learning Non-Taxonomic Relations of Ontologies: A Systematic Review. International Journal on Semantic Web and Information Systems (IJSWIS), 17(1), 97-122. http://doi.org/10.4018/IJSWIS.2021010105

Chicago

Ali, Mohamed Hassan Mohamed, et al. "Learning Non-Taxonomic Relations of Ontologies: A Systematic Review," International Journal on Semantic Web and Information Systems (IJSWIS) 17, no.1: 97-122. http://doi.org/10.4018/IJSWIS.2021010105

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Abstract

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

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