Journal
IEEE ACCESS
Volume 10, Issue -, Pages 80174-80183Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3194063
Keywords
Education; Feature extraction; Semantics; Taxonomy; Task analysis; Representation learning; Object recognition; Educational technologies; employability; knowledge graphs; representation learning
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Studies on the relationship between education and employability play a crucial role for policy makers, training institutions, companies, and students. Utilizing knowledge graphs to represent large-scale data can facilitate a better understanding of job market needs and address the skill mismatch between education and the job market. This paper provides a comprehensive review of the applications of knowledge graphs in education and employability, offering insights into promising research directions.
Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the Internet, such as online job ads, has been leveraged to better investigate this relationship. Recent studies have shown that representing this data using knowledge graphs can be useful in identifying the job market needs and establishing better assessment methods. Such studies also highlight the skill mismatch between education and the job market and help mitigate it. In this paper, we provide a comprehensive review of the applications of knowledge graphs in education and employability. We review knowledge graph based frameworks that have been proposed in previous work on different aspects of education and employability. The survey introduces new taxonomies for these applications and their methodologies, and provides a thorough outlook on promising research directions. We also present a use case to illustrate how knowledge graphs can be used to investigate the relationship between education and the job market.
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