Methodology for Similarity Assessment of Relational Data Models and Semantic Ontologies
2016 International Conference on Systems Informatics, Modelling and Simulation (SIMS 2016): Proceedings 2017
Imants Zarembo, Artis Teilāns, Knut Barghorn, Jurijs Merkurjevs, Gundega Berina

In the upcoming age of semantic web there is a large number of relational databases being widely used. When time comes for a legacy relational database to migrate to semantic web or to be integrated with it, an important issue of determining similarity (compatibility) between two data models expressed in different ways arises. The goal of this paper is to describe the methodology for similarity assessment of relational database models and semantic data models and to present an ontology matching tool research prototype. The methodology consists of a set of steps, including transformation rules for data models, whose compatibility must be assessed, to the same ontology representation and applying ontology matching techniques. The methodology enables domain experts to perform a matching task semi-automatically between a relational data model and data model expressed as an ontology. The results of the semi-automatic matching are manually verified by the domain experts. The methodology was approbated using a use case from land administration domain. In the use case compatibility of data model provided by an international standard and a relational database had to be assessed.


Atslēgas vārdi
data models, ontology matching, relational databases, schema matching
DOI
10.1109/SIMS.2016.21
Hipersaite
http://ieeexplore.ieee.org/document/7811877/

Zarembo, I., Teilāns, A., Barghorn, K., Merkurjevs, J., Berina, G. Methodology for Similarity Assessment of Relational Data Models and Semantic Ontologies. No: 2016 International Conference on Systems Informatics, Modelling and Simulation (SIMS 2016): Proceedings, Latvija, Riga, 1.-3. jūnijs, 2016. Los Alamitos: IEEE Computer Society, 2017, 119.-123.lpp. ISBN 978-1-5090-2694-4. e-ISBN 978-1-5090-2693-7. Pieejams: doi:10.1109/SIMS.2016.21

Publikācijas valoda
English (en)
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