Database-Augmented Query Representation for Information Retrieval

TL;DR

This paper introduces DAQu, a novel retrieval framework that enhances query representation by augmenting it with database-derived metadata encoded via graph-based set encoding, significantly improving retrieval accuracy.

Contribution

The paper proposes a new method for query augmentation using database metadata and a graph-based encoding strategy, addressing limitations of previous feature-based approaches.

Findings

DAQu improves retrieval performance across multiple scenarios.

Graph-based set encoding effectively handles large, unordered feature sets.

Metadata augmentation leads to significant accuracy gains.

Abstract

Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, which have been applied to diverse tasks. Yet, the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. To tackle this, previous studies have proposed expanding the query with a couple of additional (user-related) features related to it. However, they may be suboptimal to effectively augment the query, and there is plenty of other information available to augment it in a relational database. Motivated by this fact, 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 the graph-based set-encoding strategy, which considers hierarchies of features in the database without order. We validate our DAQu in diverse retrieval scenarios, demonstrating that it significantly enhances overall retrieval performance over relevant baselines.