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The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a useful mechanism for managing and exchanging data. This is the reason why bridging the gap between relational databases and ontologies has attracted the interest of the ontology community from early years, and it is commonly referred to as the database-to-ontology mapping problem. In this paper, we: (1) propose a new life cycle for ontology learning from RDBs based on the software engineering requirements; (2) describe a new method for building ontology from Relational database based on the predefined life cycle; (3) add three new semantics that can be extracted from RDB; (4) we suggest an evaluation process based on two categories of metrics: (i) conceptual ontology (TBox) evaluation metrics; (ii) factual ontology (ABox) evaluation metrics.

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In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieves highly competitive computational performance against other state-of-the-art methods in terms of load time and query response time.

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