=Paper=
{{Paper
|id=Vol-1515/poster14
|storemode=property
|title=Mapping a database schema to the structure of an existing ontology
|pdfUrl=https://ceur-ws.org/Vol-1515/poster14.pdf
|volume=Vol-1515
|dblpUrl=https://dblp.org/rec/conf/icbo/NafissiFU15
}}
==Mapping a database schema to the structure of an existing ontology==
https://ceur-ws.org/Vol-1515/poster14.pdf
Mapping a Database Schema to the Structure of an
Existing Ontology
Anahita Nafissi*, Fabio Fiorani and Björn Usadel
Plant Sciences (IBG-2), Forschungszentrum Jülich, D-52425 Jülich, Germany
* a.nafissi@fz-‐juelich.de
In this work, we describe an approach for mapping the eign key attributes, an entity relation contains non foreign
structure of a database schema to the structure of existing key attributes. For the mapping process, we do not consider
ontologies. The database contains plant traits values and relationship relations and all their attributes. Similarly, we
plant experimental history. The goal is to identify the se- extract concept - and role names of the ontology.
mantic correspondences between databases and ontologies
and provide a tool that can be more broadly adopted by the For the mapping process, we have to discover the corre-
community. The approach presented in this work is a semi- spondences between the terms of the database and the terms
automatic approach. of the ontology. For this purpose, we compare the relation -
and attribute names of the database with the concept names
In the literature, there are several approaches which map the of the ontology. The comparison is performed according to
database schema to an ontology. The underlying assumption the similarity matches. This means that we find similar
by all approaches is that the chosen ontologies model the matches among the relation and attribute names of the data-
same domain as the one modelled by the relational database base and concepts of the ontology. Then, the results should
schema. Some mapping approaches are R2O (Barrasa et al., be evaluated by a human expert (plant biologist) who is fa-
2004), DartGrid (Chen et al., 2006), Linked Data Mapper miliar with both the terms used in the database and in the
(Zhou et al., 2008), RDOTE (Vavliakis et al., 2010), ontologies. Thus, this approach is a semi-automatic ap-
RDB2OWL [Bumans & Cerans, 2010], MAPONTO [An et proach. For some ontologies the mapping results are more
al., 2006]. The difference between the above approaches is than the others. Furthermore, the human involvement re-
that some approaches are manual and some are semi- quired for mapping varies across different ontologies.
manual. Furthermore, for some approaches a human expert The softwares used for this work are Java, Protégé, SQL.
gives the correspondences between database terms and on-
tology terms.
ACKNOWLEDGEMENTS
The database schema of our Phenomis database considered This work is performed within the German-Plant-
for mapping contains plant phenotyping information and Phenotyping Network which is funded by the German Fed-
environmental information. The ontologies considered for eral Ministry of Education and Research (project identifica-
mapping are plant ontology, phenotypic quality ontology, tion number: 031A053)
plant trait ontology, plant environmental conditions, and
environment ontology. The mentioned ontologies are very
large and contain over 1000 concepts. Unlike the number of
concepts, the number of roles is very small (less than 10).
Note that the roles denote the relations between domain
objects.
In order to map the database to the ontology, we first con-
sider the schema of the database and extract relation names
and attributes of each relation. Note that the relations in a
relational schema are classified into two categories, namely
entity relations and relationship relations (Hu & Qu, 2007).
Furthermore, an attribute is also classified into two catego-
ries, namely foreign key attribute and non foreign key at-
tribute (Hu & Qu, 2007). A relationship relation is used to
connect two other relations and contains foreign key attrib-
utes. Unlike a relationship relation which contains only for-
Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes 1
Nafissi et al.
REFERENCES
An, Y., Borgida A. and Mylopoulos, J. (2006). Discovering the Semantics
of Relational Tables through Mappings, Journal on Data Semantics, VII,
pp. 1-32.
Barrasa, J., Corcho O., and Gomez-Perez A (2004). R2O, an Extensible
and Semantically Based Database-to-Ontology Mapping Language, in
Second International Workshop on Semantic Web and Databases (SWDB
2004).
Bumans, G., and Cerans, K. (2010). RDB2OWL: a Practical Approach for
Transforming RDB Data into RDF/OWL, in A. Paschke, N. Henze and T.
Pellegrini, eds. Proceedings of the 6th International Conference on Semantic
Systems (I-SEMANTICS 2010), ACM.
Chen, H., Wu, Z., Mao, Y., and Zheng, G. (2006). DartGrid: a Semantic
Infrastructure for Building Database Grid Applications, Concurrency and
Computation: Practice and Experience, 18(14), pp. 1811-1828.
Hu, W., and Qu, Y. (2007). Discovering Simple Mappings between Rela-
tional Database Schemas and Ontologies, Proceedings of the 6th Interna-
tional Semantic Web Conference and 2nd Asian Semantic Web Conference
(ISWC/ASWC2007), Busan, South Korea, Volume 4825 of LNCS, page
225—238, Berlin, Heidelberg, Springe Verlag.
Vavliakis, K.N., Grollios, T.K., and Mitkas, P.A. (2010). RDOTE-
Transforming Relational Databases into Semantic Web Data, in A. Polleres
and H. Chen, eds., Proceedings of the ISWC 2010 Posters & Demonstra-
tions Track: Collected Abstracts, pp.121-124.
Zhou, C., Xu, C., Chen, H. and Idehen, K. (2008). Browser-based Semantic
Mapping Tool for Linked Data in Semantic Web, in C. Bizer, T. Heath. K.
Idehen and T. Berners-Lee, eds., Proceedings of the WWW 2008 Work-
shop on Linked Data on the Web (LDOW 2008).
2 Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes