  In this paper, we address the problem of learning low dimension
representation of entities on relational databases consisting of multiple
tables. Embeddings help to capture semantics encoded in the database and can be
used in a variety of settings like auto-completion of tables, fully-neural
query processing of relational joins queries, seamlessly handling missing
values, and more. Current work is restricted to working with just single table,
or using pretrained embeddings over an external corpus making them unsuitable
for use in real-world databases. In this work, we look into ways of using these
attention-based model to learn embeddings for entities in the relational
database. We are inspired by BERT style pretraining methods and are interested
in observing how they can be extended for representation learning on structured
databases. We evaluate our approach of the autocompletion of relational
databases and achieve improvement over standard baselines.
