  The purpose of this paper is to outline a generalised model for representing
hybrids of relational-categorical, symbolic, perceptual-sensory and
perceptual-latent data, so as to embody, in the same architectural data layer,
representations for the input, output and latent tensors. This variety of
representation is currently used by various machine-learning models in computer
vision, NLP/NLU, reinforcement learning which allows for direct application of
cross-domain queries and functions. This is achieved by endowing a directed
Tensor-Typed Multi-Graph with at least some edge attributes which represent the
embeddings from various latent spaces, so as to define, construct and compute
new similarity and distance relationships between and across tensorial forms,
including visual, linguistic, auditory latent representations, thus stitching
the logical-categorical view of the observed universe to the
Bayesian/statistical view.
