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ontologies which will be used for indexing is a
primordial step. Our ontology modularization
approach would be useful in this context.
In many studies, the choice of the domain
ontology which will serve to represent the corpus is
dependent of the task domain itself (Vallet et al.,
2005). Thus, the reusability of the ontology for
another task or another domain is not insured.
In fields, like medicine, ontologies have
especially great size, and contain many knowledge
domains. A collection of medical documents could
be represented by the ontology. We can have a
corpus which talks about a specific disease and
another corpus which talks about treatment of this
disease. As a result of a classic semantic indexing,
the two corpuses are indexed by a single ontology.
At the end, we obtain many concepts which are
shared to represent the two corpuses. This can affect
the relevance of the document retrieved later. In this
case, our modularization approach would be useful.
In fact, in this case, we aim to extract two modules,
from this ontology, which are semantically related to
the two corpuses. Every module is a representation
space of its correspondent corpus. When a query
concerning the disease is formulated, only the
documents which are indexed semantically by the
disease module are retrieved.
6 CONCLUSIONS AND FUTURE
WORK
In this paper, we proposed a method to extract
modules from ontologies based on semantic
relations identification. We considered four semantic
relations which are: Identity, Synonymy,
Classification and Antonymy. We considered that
two concepts are relevant for the module if there
exists a semantic relation between them. We have
used Wordnet to identify the semantic relation
between the external concept (the user request) and
the internal one (the ontology concept). The result of
the extraction is a module composed from these
concepts and their definitions.
We show that the use of semantic relations
makes the method less dependent to the structure of
the ontology to modularize. It is effectively intended
to high expressive and more complex ontologies
rather than ontology structures based on
subsumption relations. The user is involved in the
modularization process but he is not supposed
knowing the components of the ontology. His needs
are expressed as a list of relevant concepts for his
purpose. Hence, the method is automatic but takes
into account the user requirements. The user here
could be a human or an application program. In fact,
the main goal of this approach is to allow programs
to extract useful modules from available ontologies
on the Web. In this way, our goal meets the
objective of the semantic Web which is to allow data
to be shared, understood and reused across
applications.
In future work, we envision to evaluate the
usefulness of our approach. For this purpose, we
have to determine the possible evaluation criteria,
including application-dependent criteria, which can
be used to determine the quality of a module. We
intend to develop an IR system for medical Web
documents using ontology modules to index the
documents. The efficiency of the approach would be
discussed in the context of experiments that aim to
measure the relevance of the retrieved documents.
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