  Information retrieval models that aim to search for the documents relevant to
the given query have shown many successes, which have been applied to diverse
tasks. However, the query provided by the user is oftentimes very short, which
challenges the retrievers to correctly fetch relevant documents. To tackle
this, existing studies have proposed expanding the query with a couple of
additional (user-related) features related to the query. Yet, they may be
suboptimal to effectively augment the query, though there is plenty of
information available to augment it in a relational database. Motivated by
this, we present a novel retrieval framework called Database-Augmented Query
representation (DAQu), which augments the original query with various
(query-related) metadata across multiple tables. In addition, as the number of
features in the metadata can be very large and there is no order among them, we
encode them with our graph-based set encoding strategy, which considers
hierarchies of features in the database without order. We validate DAQu in
diverse retrieval scenarios that can incorporate metadata from the relational
database, demonstrating that ours significantly enhances overall retrieval
performance, compared to existing query augmentation methods.
