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main phases:
• Initialization of membership values,
• Updating the membership value of concepts and
relations,
• Updating the membership value of the existing
concepts in the user’s query
• Updating the membership value of relations re-
lated to the existing concepts in the user’s query.
Fuzzy ontologies buiding method is integrated to
IR process, and returned results are classified taking
into account fuzzified relations.
So, in this work, our first contribution concerns the
fuzzy ontoly’s building process. Our method consid-
ers automatic fuzzification of a domain ontology tak-
ing into account both taxonomic and non taxonomic
relations, however, all relations are important mainly
in case of query reformulation.
Second contribution concerns the integration of
our fuzzy ontology method into the IR process. In-
deed, query reformulation is based on the weights as-
sociated to all the relations existing in the fuzzy ontol-
ogy, and this fuzzy ontology is used to classify docu-
ments by services.
Finally, the obtained results establish the great
interest and FuzzOntoEnrichIR’s contribution to im-
prove the performance of the retrieval task. Experi-
ments and evaluations have been carried out, which
highlight that overall achieved improvement are ob-
tained thanks to the integration of fuzzy ontologies
into IR process, integration of update and classifica-
tion. These components contribute to significantly in-
crease the relevance of search results, by enhancing
documents ranking as shown by the obtained results.
As an evolution of this work, integration of mod-
ular ontologies in order to facilitate the updates is in
progress. Otherwise, the ontology will be extended
to different domains so that architecture will support
a multi-domain use of the ontology. A multi-domain
retrieval based on modular and fuzzy ontologies will
be possible.
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