  The cognitive framework of conceptual spaces bridges the gap between symbolic
and subsymbolic AI by proposing an intermediate conceptual layer where
knowledge is represented geometrically. There are two main approaches for
obtaining the dimensions of this conceptual similarity space: using similarity
ratings from psychological experiments and using machine learning techniques.
In this paper, we propose a combination of both approaches by using
psychologically derived similarity ratings to constrain the machine learning
process. This way, a mapping from stimuli to conceptual spaces can be learned
that is both supported by psychological data and allows generalization to
unseen stimuli. The results of a first feasibility study support our proposed
approach.
