Bio-medical Ontologies Maintenance and Change Management

Extraction of Constraints from Biological Data 175
Rules are usually represented in the form: body → head [support, confidence].
Body and head are two arbitrary sets of data items, such that body ∩ head = Ø.
Support (s) and confidence (c) are used to measure the quality of an association
rule. They are computed as shown in equations (1) and (2).
n
=
n
s bh (1)
n
n
bh c =
(2)
b
In Eq. (1), n bh is the number of data instances that contain both body and head,
and n the cardinality of the relation (i.e., the number of data instances in the relation).
In Eq. (2), n b is the total number of data instances containing the body [12].
In Eq. (1), n bh is also called absolute support, while the support s is a relative value
with respect to the total number of tuples.
For example, the rule ((City=Paris)AND(Age=30)) → (Preferred product=Car)
[0.5%, 60%] means that thirty years old people living in Paris whose
preferred product is a car are the 0.5% of the buyers stored in the database. It
means also that the preferred product for 60% of thirty years old people living in
Paris (stored in the buyers database) is a car.
4 Constraint Extraction in Biological Data
In this section we describe how constraints can be extracted from a database using
a combination of the previously introduced techniques. Fig. 1 synthesizes the
phases of the proposed approach, exploited in [3] for a different application. The
method is based on association rule extraction, based on the Apriori algorithm [1].
Any other association rule mining algorithm may be substituted as a building
block. Association rules are extracted form the data. Next, tuple constraints and
functional dependencies are identified by analyzing the extracted rules.
If the database constraints and dependencies are already known, they can be
used as a criterion to evaluate the accuracy of the method. In this case, when a table
row does not satisfy a tuple constraint or a functional dependency, its data is
not correct.
Constraint knowledge may be exploited to improve data quality and integration
in database design, and to perform query optimization and dimensional reduction.
We show an application of our method for identifying anomalies with respect to
the detected constraints which can be used to improve domain knowledge.
4.1 Quasi Tuple Constraints and Quasi Functional Dependencies
The concepts of support and confidence of a detected rule are used to determine its
frequency and its strength. An association rule with confidence equal to 1 means
that there is a tuple constraint between the attribute values in the head and the ones