Bio-medical Ontologies Maintenance and Change Management

174 D. Apiletti et al.
Constraints may be classified as follows [4].
• Intra-relational constraints if they are validated over the tuples of a single relation.
Particular cases of such constraints are (in decreasing order of specificity)
the following.
– Domain constraints (also known as value constraints or attribute constraints)
apply to the values of a single attribute. With respect to the relation
described above, the following domain constraints hold:
� (Age ≥ 18) AND (Age ≤ 25)
� Mail must contain the ‘@’ symbol.
– Tuple constraints apply to combinations of different attribute values within
a single tuple. They must hold separately on each tuple. For example:
� NOT( (City=Rome) AND (Country != Italy) ), which means that if the
City is Rome, then the Country must be Italy (i.e., it is not possible that
if the city is Rome, the country is not Italy).
– Functional dependencies apply to combination of attributes and must hold
for all the tuples of the relation. Key constraints are the most important
functional dependency. For example:
� StudentID → Name, which means that given a value of the StudentID
attribute, the value of the Name attribute is univocally determined. If
StudentID is also the key of the relation, this means that all the StudentID
values must be different, i.e., unique within the considered relation.
Functional dependencies can be seen as a generalization of tuple constraints.
If there is a tuple constraint between every value of two attributes, then
there is also a functional dependency between the two attributes.
• Inter-relational constraints are integrity constraints which are validated over
the tuples of different database relations. The most well known are referential
integrity constraint, which describe the relationship among attributes in different
relations. For example, if a mark relation stores marks for all students, the
StudentID attribute may be exploited to establish a relationship between the
student and mark relations.
In this work, we focus on intra-relational constraints, in particular on tuple constraints
and functional dependencies.
3.3 Association Rules
Association rule discovery is an important data mining technique, which is commonly
used for local pattern detection in unsupervised learning systems. It shows
attribute values that occur together in a given dataset. These combinations of values
are useful for finding correlations among sets of items in transactional or relational
databases. Association rules describe the co-occurrence of data items (i.e.,
couples in the form (attribute, value)) in a large amount of collected data [2].