Exploring logical and hierarchical information to map relational databases into ontologies

Abstract

Ontologies are formal specifications of conceptualizations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilization of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self)hierarchies. We validate our approach with 2 experiments from the healthcare domain as input.

A novel approach for learning ontology from relational database: from the construction to the evaluation

“…Considerable amount of studies [18][19][20][21][22][23][24][25][26][27][28][29][30] have been conducted on building ontologies from RDBs using SQL-DDL [35]. While these studies share the common objective of converting RDB into Ontology, they differ in the process used as well as the metadata extracted and the mapping rules proposed.…”

“…Despite the significant progress made during the last few years and the wide number of proposed approaches [18][19][20][21][22][23][24][25][26][27][28][29][30], there are still many issues that have not been sufficiently addressed. First, all the existing works [18][19][20][21][22][23][24][25][26][27][28][29][30] focus only on generating A-Box or T-Box [31] and ignore the integration process between these two components.…”

“…Despite the significant progress made during the last few years and the wide number of proposed approaches [18][19][20][21][22][23][24][25][26][27][28][29][30], there are still many issues that have not been sufficiently addressed. First, all the existing works [18][19][20][21][22][23][24][25][26][27][28][29][30] focus only on generating A-Box or T-Box [31] and ignore the integration process between these two components. Second, the majority of these studies [18][19][20][21][22][23][24][25][26][27][28][29][30] mainly focused on the process of building ontologies from relational database without covering the maximum semantics resided in the database [32].…”

“…First, all the existing works [18][19][20][21][22][23][24][25][26][27][28][29][30] focus only on generating A-Box or T-Box [31] and ignore the integration process between these two components. Second, the majority of these studies [18][19][20][21][22][23][24][25][26][27][28][29][30] mainly focused on the process of building ontologies from relational database without covering the maximum semantics resided in the database [32]. Furthermore, all these studies focus only on describing the process of generating ontology from RDB, while they did not define a life cycle for describing the most common scenarios that arise during the creation of the ontology from RDB [33].…”

Ontology Based Governance for Employee Services

“…The solution we propose can also be considered in conjunction with other research papers that explore the potential for exporting data from relational databases into ontologies [6,[31][32][33][34]. In this way, it is possible to utilize the huge volume of data that already exists in relational databases, to facilitate the transition of information systems using relational databases to semantic ontology systems.…”