Learning Non-Taxonomic Relations of Ontologies
| Autor: | Mohamed Kholief, Yasser F. Hassan, Said Fathalla, Mohamed Ali |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Ontology learning
Computer Networks and Communications Computer science Process (engineering) 02 engineering and technology Data science Domain (software engineering) 020204 information systems 0202 electrical engineering electronic engineering information engineering Information system Key (cryptography) Semantic memory 020201 artificial intelligence & image processing Set (psychology) Representation (mathematics) Information Systems |
| Zdroj: | International Journal on Semantic Web and Information Systems. 17:97-122 |
| ISSN: | 1552-6291 1552-6283 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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