Bio-medical Ontologies Maintenance and Change Management

Extraction of Constraints from Biological Data 177
The primary key is AnimalID. Thus, we assume that the following functional
dependencies hold at design time:
• AnimalID → Species,
• AnimalID → Class,
• AnimalID → Reproduction.
Furthermore, every rule that involves an attribute value of AnimalID is a tuple
constraint.
According to the nature and the content of the considered relation, we also expect
to find the functional dependency Class → Reproduction, because given a
class, we can always know the way of reproduction. Hence, we expect exclusively
to find association rules with confidence 100% between the attributes Class and
Reproduction.
Since in biological databases data dependencies may be incomplete, due to the
complexity of the stored data, it is possible that none of the discovered rules have
a confidence or dependency degree equal to 1. To address this issue, we extract
also association rules with confidence lower than 100%. Considering the subset of
the relation Animal shown in Table 1, we can find two classes (Mammalia and
Aves) which correspond to both ways of reproduction. Table 2 reports examples of
associations rules found in the database (support and confidence are the values extracted
by the mining algorithm applied on the entire dataset whose portion is reported
in Table 1).
Table 1. A portion of the Animal table
AnimalID Species Class Reproduction
1 Felis catus Mammalia vivipary
2 Ornithorhynchus Mammalia ovipary
3 Mus musculus castaneus Mammalia vivipary
… … … …
100 Passer Domesticus Aves vivipary
101 Eurypyga Helias Aves ovipary
… … … …
Table 2. Examples of association rules found in the Animal table
Body Head Sup Conf
Class = Mammalia Reproduction = vivipary 45.1% 75.2%
Class = Mammalia Reproduction = ovipary 14.9% 24.8%
Class = Aves Reproduction = ovipary 28.7% 99.7%
Class = Aves Reproduction = vivipary 0.1% 0.3%