Much of the world's most valued data is stored in relational databases and
data warehouses, where the data is organized into many tables connected by
primary-foreign key relations. However, building machine learning models using
this data is both challenging and time consuming. The core problem is that no
machine learning method is capable of learning on multiple tables
interconnected by primary-foreign key relations. Current methods can only learn
from a single table, so the data must first be manually joined and aggregated
into a single training table, the process known as feature engineering. Feature
engineering is slow, error prone and leads to suboptimal models. Here we
introduce an end-to-end deep representation learning approach to directly learn
on data laid out across multiple tables. We name our approach Relational Deep
Learning (RDL). The core idea is to view relational databases as a temporal,
heterogeneous graph, with a node for each row in each table, and edges
specified by primary-foreign key links. Message Passing Graph Neural Networks
can then automatically learn across the graph to extract representations that
leverage all input data, without any manual feature engineering. Relational
Deep Learning leads to more accurate models that can be built much faster. To
facilitate research in this area, we develop RelBench, a set of benchmark
datasets and an implementation of Relational Deep Learning. The data covers a
wide spectrum, from discussions on Stack Exchange to book reviews on the Amazon
Product Catalog. Overall, we define a new research area that generalizes graph
machine learning and broadens its applicability to a wide set of AI use cases.

关系数据库中的数据存储方式使得使用机器学习模型变得具有挑战性和耗时，通过引入关系深度学习（RDL）方法，我们可以直接学习跨多个表格布置的数据，而无需进行手动特征工程。关系深度学习通过自动学习图引导，从而提取利用所有输入数据的表示，从而构建更准确的模型且速度更快。

关系深度学习：关系数据库上的图表示学习

Relational Deep Learning: Graph Representation Learning on Relational  Databases

Recently, deep models have had considerable success in several tasks,
especially with low-level representations. However, effective learning from
sparse noisy samples is a major challenge in most deep models, especially in
domains with structured representations. Inspired by the proven success of
human guided machine learning, we propose Knowledge-augmented Column Networks,
a relational deep learning framework that leverages human advice/knowledge to
learn better models in presence of sparsity and systematic noise.

本篇论文提出了基于人工引导学习的知识增强列网络（Knowledge-augmented Column Networks），这是一个关系性深度学习框架，可以在数据稀疏和受到系统噪声的情况下利用人类的知识来学习更好的模型。