From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics

TL;DR

This paper introduces Retrieval-Augmented Modeling (RAM), a novel framework that combines semantic attribute signals and graph structure to improve relational data analytics with deep learning.

Contribution

RAM is the first framework to integrate attribute semantics with graph structure using retrieval techniques for relational data modeling.

Findings

RAM outperforms existing methods on five real-world datasets.

The retrieval-based augmentations improve semantic relevance estimation.

The proposed architecture achieves state-of-the-art results in relational data tasks.

Abstract

Relational data stored in RDBMS is foundational to many real-world applications across domains such as e-commerce, finance, and sociality. While deep neural networks (DNNs) have achieved strong performance on tabular data with a single table, extending these models to relational databases is challenging due to the normalized multi-table structure and complex inter-table relationships. Existing approaches often rely strictly on schema-defined graphs, which overlook implicit semantic signals embedded in tuple attributes and suffer from rigid connectivity. In this work, we propose Retrieval-Augmented Modeling (RAM), a novel framework that combines graph structure with attribute semantics for relational data analytics. RAM treats tuple attributes as tokens and uses random walks to construct contextual documents, enabling the use of information retrieval techniques to estimate semantic relevance between tuples. Building on these documents, we introduce two retrieval-based augmentations: ATRA, which leverages intra-table relevance for contrastive learning, and ETRA, which links semantically related tuples across tables to enhance graph connectivity. Then, we propose a layer-wise model architecture tailored for relational data, which involves attribute embedding, feature integration, and graph aggregation layers to enable expressive and flexible representation learning. Extensive experiments on five real-world relational databases demonstrate that RAM consistently outperforms existing baselines in diverse prediction tasks, establishing a state-of-the-art for relational data analytics.