  Data in Knowledge Graphs often represents part of the current state of the
real world. Thus, to stay up-to-date the graph data needs to be updated
frequently. To utilize information from Knowledge Graphs, many state-of-the-art
machine learning approaches use embedding techniques. These techniques
typically compute an embedding, i.e., vector representations of the nodes as
input for the main machine learning algorithm. If a graph update occurs later
on -- specifically when nodes are added or removed -- the training has to be
done all over again. This is undesirable, because of the time it takes and also
because downstream models which were trained with these embeddings have to be
retrained if they change significantly. In this paper, we investigate embedding
updates that do not require full retraining and evaluate them in combination
with various embedding models on real dynamic Knowledge Graphs covering
multiple use cases. We study approaches that place newly appearing nodes
optimally according to local information, but notice that this does not work
well. However, we find that if we continue the training of the old embedding,
interleaved with epochs during which we only optimize for the added and removed
parts, we obtain good results in terms of typical metrics used in link
prediction. This performance is obtained much faster than with a complete
retraining and hence makes it possible to maintain embeddings for dynamic
Knowledge Graphs.
