  With the increasing demand of intelligent systems capable of operating in
different contexts (e.g. users on the move) the correct interpretation of the
user-need by such systems has become crucial to give consistent answers to the
user questions. The most effective applications addressing such task are in the
fields of natural language processing and semantic expansion of terms. These
techniques are aimed at estimating the goal of an input query reformulating it
as an intent, commonly relying on textual resources built exploiting different
semantic relations like \emph{synonymy}, \emph{antonymy} and many others. The
aim of this paper is to generate such resources using the labels of a given
taxonomy as source of information. The obtained resources are integrated into a
plain classifier for reformulating a set of input queries as intents and
tracking the effect of each relation, in order to quantify the impact of each
semantic relation on the classification. As an extension to this, the best
tradeoff between improvement and noise introduction when combining such
relations is evaluated. The assessment is made generating the resources and
their combinations and using them for tuning the classifier which is used to
reformulate the user questions as labels. The evaluation employs a wide and
varied taxonomy as a use-case, exploiting its labels as basis for the semantic
expansion and producing several corpora with the purpose of enhancing the
pseudo-queries estimation.
