  We study the problem of computing an embedding of the tuples of a relational
database in a manner that is extensible to dynamic changes of the database.
Importantly, the embedding of existing tuples should not change due to the
embedding of newly inserted tuples (as database applications might rely on
existing embeddings), while the embedding of all tuples, old and new, should
retain high quality. This task is challenging since state-of-the-art embedding
techniques for structured data, such as (adaptations of) embeddings on graphs,
have inherent inter-dependencies among the embeddings of different entities. We
present the FoRWaRD algorithm (Foreign Key Random Walk Embeddings for
Relational Databases) that draws from embedding techniques for general graphs
and knowledge graphs, and is inherently utilizing the schema and its key and
foreign-key constraints. We compare FoRWaRD to an alternative approach that we
devise by adapting node embeddings for graphs (Node2Vec) to dynamic databases.
We show that FoRWaRD is comparable and sometimes superior to state-of-the-art
embeddings in the static (traditional) setting, using a collection of
downstream tasks of column prediction over geographical and biological domains.
More importantly, in the dynamic setting FoRWaRD outperforms the alternatives
consistently and often considerably, and features only a mild reduction of
quality even when the database consists of mostly newly inserted tuples.
