sUniversity (University#1, name, PoitiersUniver-
sity) and (University#3, name, ToursUniversity).
Given that the idUniv property is computed as the
primary key of the University class, the loaded
triplets present a violation case of the uniqueness
constraint. Indeed, the two universities are dif-
ferent but they have the same identifier. In or-
der to detect such data, we propose the rule def-
inition to address the primary key violation for
each stored ontological class. The definition of
such a rule requires the creation of a rulebase, fol-
lowed by an index creation. This rule raises the
uniquenessconstraintviolation case by generating
a comment to prevent users about the data incon-
sistency existence as follows: (s, :comment, ’In-
consistency: violation of primary key constraint’)
where s presents the inconsistent instance. Once
the inconsistent data error is raised, the user will
decide if the data should be deleted or not.
4.2 Synthesis
In the most S D B , storage models are frozen. For ex-
ample, in Oracle 11g, the vertical representation is
used for the storage of ontological concepts and in-
stances referencing them. Therefore, our approach
can not be applied globally. This does not elimi-
nate the contributions offered by our methodology for
such databases. Indeed, based on the class dependen-
cies, the class’s types are identified and stored in the
ontology-based data models. The property functional
dependencies modeling allows to compute primary
keys for each ontological class and subsequently, to
store them in the S D B . Based on the basis of these
keys, the rules helping to detect a set of inconsistent
data may be defined. They ensure that the stored data
correspond to the boundaries of the modeled universe
and reduce the inconsistency and redundancy in the
S D B models.
5 CONCLUSIONS
This paper presents a complete methodology for de-
signing S D B covering the three main steps of tradi-
tional database design: conceptual, logical and physi-
cal. Ontology is the core of our design methodology.
Traditional definition of ontology is enriched by de-
pendencies between properties and classes. These de-
pendencies are exploited to generate consistent S D B .
They are used to identify the canonical concepts that
have to be stored in the database. The non-canonical
concepts are managed by relational views. A case
study showing the deployment of our S D B obtained
by our methodology in the semantic Oracle DBMS is
proposed.
Currently, we are developing a design tool to as-
sist designers during the S D B design process. Also,
we are working on incorporating optimization struc-
ture selection during the physical design.
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