Tissot, H;
Huve, CAG;
Peres, LM;
Fabro, MDD;
(2019)
Exploring logical and hierarchical information to map relational databases into ontologies.
International Journal of Metadata, Semantics and Ontologies
, 13
(3)
pp. 191-208.
10.1504/IJMSO.2019.099834.
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Abstract
Ontologies are formal specifications of conceptualisations. 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 utilisation 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 two experiments from the healthcare domain as input.
Type: | Article |
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Title: | Exploring logical and hierarchical information to map relational databases into ontologies |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1504/IJMSO.2019.099834 |
Publisher version: | https://doi.org/10.1504/IJMSO.2019.099834 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | ontologies; rule-based integration; logical model; hierarchical structures. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10076649 |
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