Ontology learning from relational database: a review

Abstract

A relational database (RDB) is a digital database that uses components (such as constraints, tables, keys, etc.) to manage data in a structured manner. Because of these components, RDBs are considered ’poor’ from a semantic point of view, precisely because of the structure-oriented nature of the components used. One way to eliminate this limitation is to transform the RDB into an ontology. The purpose of this article is to review the different approaches existing in the literature to extract data from an RDB and convert it into ontology instances. Two approaches are used to integrate the mapping between RDBs and ontologies. The first allows ontologies to be extracted from an RDB, the second consists of a mapping of the relational database to an existing ontology. Our proposed review focuses on methods for creating a specific ontology from an RDB. The proposed review examines this field, classifying the methods that will be analyzed according to their inputs and outputs. Such classification may be useful for understanding the usability of methods. The aim is to critically review existing studies to help outline this research topic’s progress and identify methods’ gaps and functionalities.