将表格数据转为图结构，生成无需训练的通用嵌入表示。
Universal Embeddings of Tabular Data
- 先构图再用图自编码器学习实体嵌入，最后聚合得行级表示
- 在真实数据集上优于现有通用表格嵌入方法
- 适合未预设任务的工业场景，支持新样本快速嵌入
关系型数据库中的表格数据占工业数据的重要部分，因此分析与理解表格数据至关重要。表格数据的应用任务多样且常在数据库建立时未被明确。为此，我们提出一种新型框架，用于生成无需任务指定的通用表格数据嵌入，以支持下游任务而无需预先定义目标。该方法将表格数据转换为图结构，利用图自编码器生成实体嵌入，并进一步聚合得到每行数据（即每个数据样本）的嵌入表示。这一两步流程的优势在于，对于包含相似实体的未见样本，可无需额外训练直接嵌入。随后，通过嵌入空间中的基于距离的相似性度量，即可完成回归、分类或异常检测等下游任务。在真实数据集上的实验表明，该方法在性能上优于现有通用表格数据嵌入技术。
原文摘要 · Abstract (English)
Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are often not specified when setting up an industrial database. To address this, we present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets. Our method transforms tabular data into a graph structure, leverages Graph Auto-Encoders to create entity embeddings, which are subsequently aggregated to obtain embeddings for each table row, i.e., each data sample. This two-step approach has the advantage that unseen samples, consisting of similar entities, can be embedded without additional training. Downstream tasks such as regression, classification or outlier detection, can then be performed by applying a distance-based similarity measure in the embedding space. Experiments on real-world datasets demonstrate that our method achieves superior performance compared to existing universal tabular data embedding techniques.
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