  Recent work in Machine Learning and Computer Vision has provided evidence of
systematic design flaws in the development of major object recognition
benchmark datasets. One such example is ImageNet, wherein, for several
categories of images, there are incongruences between the objects they
represent and the labels used to annotate them. The consequences of this
problem are major, in particular considering the large number of machine
learning applications, not least those based on Deep Neural Networks, that have
been trained on these datasets. In this paper we posit the problem to be the
lack of a knowledge representation (KR) methodology providing the foundations
for the construction of these ground truth benchmark datasets. Accordingly, we
propose a solution articulated in three main steps: (i) deconstructing the
object recognition process in four ordered stages grounded in the philosophical
theory of teleosemantics; (ii) based on such stratification, proposing a novel
four-phased methodology for organizing objects in classification hierarchies
according to their visual properties; and (iii) performing such classification
according to the faceted classification paradigm. The key novelty of our
approach lies in the fact that we construct the classification hierarchies from
visual properties exploiting visual genus-differentiae, and not from
linguistically grounded properties. The proposed approach is validated by a set
of experiments on the ImageNet hierarchy of musical experiments.
