  Relational representation learning has lately received an increase in
interest due to its flexibility in modeling a variety of systems like
interacting particles, materials and industrial projects for, e.g., the design
of spacecraft. A prominent method for dealing with relational data are
knowledge graph embedding algorithms, where entities and relations of a
knowledge graph are mapped to a low-dimensional vector space while preserving
its semantic structure. Recently, a graph embedding method has been proposed
that maps graph elements to the temporal domain of spiking neural networks.
However, it relies on encoding graph elements through populations of neurons
that only spike once. Here, we present a model that allows us to learn spike
train-based embeddings of knowledge graphs, requiring only one neuron per graph
element by fully utilizing the temporal domain of spike patterns. This coding
scheme can be implemented with arbitrary spiking neuron models as long as
gradients with respect to spike times can be calculated, which we demonstrate
for the integrate-and-fire neuron model. In general, the presented results show
how relational knowledge can be integrated into spike-based systems, opening up
the possibility of merging event-based computing and relational data to build
powerful and energy efficient artificial intelligence applications and
reasoning systems.
