Bio-medical Ontologies Maintenance and Change Management

172 D. Apiletti et al.
Along with the ongoing trend to use the XML format in biological databases,
some kinds of constraints for XML data have been explored by recent research
papers [10][19], such as key, inclusion, inverse and path constraints. Instead functional
dependencies other than key dependencies have been little investigated. In
[14] the authors address this issue with a subgraph-based approach which captures
new kinds of functional dependencies useful in designing XML documents. Furthermore,
they analyze various properties such as expressiveness, semantic behaviour
and axiomatizations.
Constraints in hierarchically structured data have been discussed in [13]. In
particular, they focus on functional and key dependencies and investigate how
constraints can be used to check the consistency of data being exchanged among
different sources. This leads to the constraint propagation problem, which is addressed
in the form of propagating XML keys to relational views by providing
two algorithms. The importance of translating constraints in data exchanges is
also discussed in [9], whose work proposes a suitable language for this task. The
proposed language is able to express a wide variety of database constraints and
transformations and its development was motivated by experiences on biomedical
databases in informatic support to genome research centers.
In [8] the authors introduce the notion of pseudo-constraints, which are predicates
having significantly few violations. This is a concept similar to the quasi
functional dependencies, but they define pseudo-constraints on the Entity-
Relationship model, whereas we use association rules to define quasi functional
dependencies. Furthermore, they focus on cyclic pseudo-constraints and propose
an algorithm for extracting this kind of cyclic pattern. On the contrary, our notion
of dependency is an implication between sets of elements and is not bound to the
structure of the data source used to mine the pattern.
In [7] quasi functional dependencies have been exploited to detect anomalies in
the data. On the contrary, in this work the focus is on the extraction of constraints
and the anomaly detection is only a further possible application of our method.
3 Background
In this section we provide an overview of the main concepts behind the successful
discovery of constraints. We start with a survey of the relational model, which is
widely adopted in the database design and whose strongly structured format eases
the definition of constraints. Then we focus on a particular kind of constraints: integrity
constraints and their subtypes. Eventually we conclude with an introduction
to association rules, a well-established data mining technique for inferring
unknown correlations from data.
3.1 Relational Model
There are several ways to describe a database at a logic level, e.g., hierarchical, relational,
or object oriented. Nowadays the relational model is the most widely