  Despite the recent success of reconciling spike-based coding with the error
backpropagation algorithm, spiking neural networks are still mostly applied to
tasks stemming from sensory processing, operating on traditional data
structures like visual or auditory data. A rich data representation that finds
wide application in industry and research is the so-called knowledge graph - a
graph-based structure where entities are depicted as nodes and relations
between them as edges. Complex systems like molecules, social networks and
industrial factory systems can be described using the common language of
knowledge graphs, allowing the usage of graph embedding algorithms to make
context-aware predictions in these information-packed environments. We propose
a spike-based algorithm where nodes in a graph are represented by single spike
times of neuron populations and relations as spike time differences between
populations. Learning such spike-based embeddings only requires knowledge about
spike times and spike time differences, compatible with recently proposed
frameworks for training spiking neural networks. The presented model is easily
mapped to current neuromorphic hardware systems and thereby moves inference on
knowledge graphs into a domain where these architectures thrive, unlocking a
promising industrial application area for this technology.
