Daily, a lot of data is produced by every institution or company.
To satisfy the storage requirements, these
organizations use most of the times relational databases, which are
quite efficient to save and to manipulate
structured data. Unstructured data (appearing inside
documents) is stored in plain or annotated text files.
There is a problem when these organizations require an integrated view
of their heterogeneous information systems. It
is necessary to query/exploit every data source, but the access to
each information system is different. In this
situation, there is a need for an approach that extracts
the information from those resources and fuses it.
Topic Maps are a good solution to organize concepts, and the
relationships between those concepts, because
they follow a standard notation -- ISO/IEC 13250 --
for interchangeable knowledge representation.
Topic Maps are composed of topics and associations giving rise to
structured semantic network that gathers information concerned
with certain domain. This hierarchical topic
network can represent an ontology. This is the reason why we are using
successfully, for some years, this technology for
classification and integration of documents in
the area of digital archiving.
However, the process of ontology development based on topic maps is
complex, time consuming, and it requires a lot
of human and financial resources, because they can have a lot of
topics and associations, and the number of
resources can be very large.
To overcome this problem,
possible the Topic Maps extraction, validation,
storage, and browsing. It is composed of three main modules: (1)
Oveia extracts data, from
heterogeneous information systems, according to an ontology
specification, and stores it in a topic map;
(2) XTche validates the generated
topic map, according to a constraint
specification; (3) Ulisses browses the topic map, giving
a conceptual view over the resources.
us achieve the semantic interoperability in heterogeneous information
systems because the relevant data, according to
the desired information defined in an ontology
specification, is extracted and stored in a topic map. The environment
validates this generated topic map against a
set of rules defined in a constraint language. That topic map provides
information fragments (the data itself) linked by specific
relation to concepts at different levels of
abstraction. Moreover, the navigation over the topic map is led by a
semantic network and provides an homogeneous
view over the resources -- this justifies our decision of call it
Enabling to create a virtual map of information, the information
systems are kept in their original form, they
are not changed. Then, the same resource can be used by
different ways, for different topic maps. As it
is possible and easy to change the map itself, data reuse is