  Neural Multi-Space (NeMuS) is a weighted multi-space representation for a
portion of first-order logic designed for use with machine learning and neural
network methods. It was demonstrated that it can be used to perform reasoning
based on regions forming patterns of refutation and also in the process of
inductive learning in ILP-like style. Initial experiments were carried out to
investigate whether a self-organizing the approach is suitable to generate
similar concept regions according to the attributes that form such concepts. We
present the results and make an analysis of the suitability of the method in
the process of inductive learning with NeMuS.
