Using decision trees to learn ontology taxonomies from relational databases

被引：0

作者：

Sbai, Sara ^{[1
]}

Chabih, Oussama ^{[1
]}

Louhdi, Mohammed Reda Chbihi ^{[2
]}

Behja, Hicham ^{[1
]}

Zemmouri, El Moukhtar ^{[3
]}

Trousse, Brigitte ^{[4
]}

机构：

[1] Hassan II Univ, LRI Lab, ENSEM, Casablanca, Morocco

[2] Hassan II Univ, Fac Sci Ain Chock, Res Lab Comp Sci Innovat, Casablanca, Morocco

[3] Moulay Ismail Univ, LM2I Lab, ENSAM, Meknes, Morocco

[4] Inria Sophia Antipolis Mediterranee, AxIS Res Project Team, Sophia Antipolis, France

来源：

2020 6TH IEEE CONGRESS ON INFORMATION SCIENCE AND TECHNOLOGY (IEEE CIST'20) | 2020年

关键词：

Ontologies; Relational databases; Taxonomies; Decision trees; Classification;

D O I：

10.1109/CIST49399.2021.9357191

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Relational databases are widely used; they are at the backend of the majority of information systems. However, these databases are semantically poor. To solve this issue, it is necessary to build ontologies. In this paper, we propose an automatic approach to learn ontologies from relational databases by using classification techniques, more specifically decision trees. Finally, we evaluate our approach by conducting tests and comparing our results with results from previous works. The results were satisfactory in terms of extracting taxonomies from relational databases.

引用

页码：54 / 58

页数：5

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