  The focus of this paper is the calculation of similarity between two concepts
from an ontology for a Human-Like Interaction system. In order to facilitate
this calculation, a similarity function is proposed based on five dimensions
(sort, compositional, essential, restrictive and descriptive) constituting the
structure of ontological knowledge. The paper includes a proposal for computing
a similarity function for each dimension of knowledge. Later on, the similarity
values obtained are weighted and aggregated to obtain a global similarity
measure. In order to calculate those weights associated to each dimension, four
training methods have been proposed. The training methods differ in the element
to fit: the user, concepts or pairs of concepts, and a hybrid approach. For
evaluating the proposal, the knowledge base was fed from WordNet and extended
by using a knowledge editing toolkit (Cognos). The evaluation of the proposal
is carried out through the comparison of system responses with those given by
human test subjects, both providing a measure of the soundness of the procedure
and revealing ways in which the proposal may be improved.
