This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/17573 in citations.
Ontologies for resolving semantic heterogeneity in information integration among plant phenomics databases
Ontologies for resolving semantic heterogeneity in information integration among plant phenomics databases
Increasing amounts of heterogeneous data are produced every year by plant researchers. For data management relational databases with application-specific schemas are mainly used in this field. However, due to absence of widely shared standards, data integration and exchange between independently dev...
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Personal Name(s): | Nafissi, Anahita (Corresponding author) |
---|---|
Bruns, Benjamin / Fiorani, Fabio | |
Contributing Institute: |
Pflanzenwissenschaften; IBG-2 |
Imprint: |
Bonn
Ges. für Informatik
2018
|
Physical Description: |
167 - 170 |
ISBN: |
978-3-88579-672-5 |
Conference: | 38. GIL-Jahrestagung, Kiel (Germany), 2018-02-26 - 2018-02-27 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Plant Science |
Series Title: |
Lecture Notes in Informatics
278 |
Link: |
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Publikationsportal JuSER |
Increasing amounts of heterogeneous data are produced every year by plant researchers. For data management relational databases with application-specific schemas are mainly used in this field. However, due to absence of widely shared standards, data integration and exchange between independently developed and heterogeneous databases becomes very challenging. A critical point is to achieve semantic interoperability among these databases. The authors propose to use Semantic Web features for this integration task. Ontologies are the main core of the Semantic Web and are suitable to resolve semantic heterogeneity. In this work a semi-automated ontology based approach is defined for integrating heterogeneous data stored in distributed phenomics databases. The results of a real-world case study show that this approach creates reasonable semantic correspondences between domain-specific databases and publicly available ontologies and can significantly save time compared to classic (specification-driven) engineering approaches. |