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Authors: Zineb Elkaimbillah 1 ; Maryem Rhanoui 2 ; Mounia Mikram 2 and Bouchra El Asri 1

Affiliations: 1 National School of Computer Science and Systems Analysis ; 2 School of Information Sciences

Keyword(s): Knowledge Graph, Educational Knowledge Graph, Knowledge Graph Embedding, Knowledge Graph Application

Abstract: Knowledge graph (KG) technologies are improving Artificial Intelligence. It can effectively expand the breadth of search results. Therefore, KGs continue to solve several problems in different domains, including the education field. The application of educational KGs to learning systems has recently been expanded due to increased demand in the education sector and the importance of KGs application to learning systems. In this article, we present the knowledge Graph approach, the methodology of KG development, and analyze each step. Also, we discuss the popular KG Embedding models. We provide a comparative study of KG models in the education field.

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Paper citation in several formats:
Elkaimbillah, Z.; Rhanoui, M.; Mikram, M. and El Asri, B. (2022). Comparative Study of Knowledge Graph Models in Education Domain. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML; ISBN 978-989-758-559-3, SciTePress, pages 339-344. DOI: 10.5220/0010733800003101

@conference{bml22,
author={Zineb Elkaimbillah. and Maryem Rhanoui. and Mounia Mikram. and Bouchra {El Asri}.},
title={Comparative Study of Knowledge Graph Models in Education Domain},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML},
year={2022},
pages={339-344},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010733800003101},
isbn={978-989-758-559-3},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML
TI - Comparative Study of Knowledge Graph Models in Education Domain
SN - 978-989-758-559-3
AU - Elkaimbillah, Z.
AU - Rhanoui, M.
AU - Mikram, M.
AU - El Asri, B.
PY - 2022
SP - 339
EP - 344
DO - 10.5220/0010733800003101
PB - SciTePress

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