  Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Latent variable
models are well suited to deal with the high dimensionality and sparsity of
typical knowledge graphs. In recent publications the embedding models were
extended to also consider time evolutions, time patterns and subsymbolic
representations. In this paper we map embedding models, which were developed
purely as solutions to technical problems for modelling temporal knowledge
graphs, to various cognitive memory functions, in particular to semantic and
concept memory, episodic memory, sensory memory, short-term memory, and working
memory. We discuss learning, query answering, the path from sensory input to
semantic decoding, and the relationship between episodic memory and semantic
memory. We introduce a number of hypotheses on human memory that can be derived
from the developed mathematical models.
