  Recently, link prediction algorithms based on neural embeddings have gained
tremendous popularity in the Semantic Web community, and are extensively used
for knowledge graph completion. While algorithmic advances have strongly
focused on efficient ways of learning embeddings, fewer attention has been
drawn to the different ways their performance and robustness can be evaluated.
In this work we propose an open-source evaluation pipeline, which benchmarks
the accuracy of neural embeddings in situations where knowledge graphs may
experience semantic and structural changes. We define relation-centric
connectivity measures that allow us to connect the link prediction capacity to
the structure of the knowledge graph. Such an evaluation pipeline is especially
important to simulate the accuracy of embeddings for knowledge graphs that are
expected to be frequently updated.
