Abstract: A huge amount of data is being generated every day from different sources. Access to these data can be very valuable for decision-making. Nevertheless, the extraction of information of interest remains a major challenge given a large number of heterogeneous databases. Building shareable and (re)usable data access mechanisms including automated verification and inference mechanisms for knowledge discovery needs to use a common knowledge model with a secure, coherent, and efficient database. For this purpose, an ontology provides an interesting knowledge model and a relational database provides an interesting storage solution. Many papers propose methods for converting ontology to a relational database. This paper describes issues, challenges, and trends derived from the evaluation of 10 methods using 23 criteria. Following this study, this paper shows that none of the methods are complete as well as the conversion process does not use the full expressivity of ontology to derive a complete relational schema including advanced constraints and modification procedures. Thus, more work must be done to decrease the gap between ontologies, a relation database.

Christina Khnaisser, Luc Lavoie, Anita Burgun and Jean-Francois Ethier, “Generating Relational Database using Ontology Review” International Journal of Advanced Computer Science and Applications(ijacsa), 9(6), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090620

@article{Khnaisser2018,
title = {Generating Relational Database using Ontology Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090620},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090620},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {6},
author = {Christina Khnaisser and Luc Lavoie and Anita Burgun and Jean-Francois Ethier}
}