  Textual queries are largely employed in information retrieval to let users
specify search goals in a natural way. However, differences in user and system
terminologies can challenge the identification of the user's information needs,
and thus the generation of relevant results. We argue that the explicit
management of ontological knowledge, and of the meaning of concepts (by
integrating linguistic and encyclopedic knowledge in the system ontology), can
improve the analysis of search queries, because it enables a flexible
identification of the topics the user is searching for, regardless of the
adopted vocabulary. This paper proposes an information retrieval support model
based on semantic concept identification. Starting from the recognition of the
ontology concepts that the search query refers to, this model exploits the
qualifiers specified in the query to select information items on the basis of
possibly fine-grained features. Moreover, it supports query expansion and
reformulation by suggesting the exploration of semantically similar concepts,
as well as of concepts related to those referred in the query through thematic
relations. A test on a data-set collected using the OnToMap Participatory GIS
has shown that this approach provides accurate results.
