We develop a generative attention-based approach to modeling structured
entities comprising different property types, such as numerical, categorical,
string, and composite. This approach handles such heterogeneous data through a
mixed continuous-discrete diffusion process over the properties. Our flexible
framework can model entities with arbitrary hierarchical properties, enabling
applications to structured Knowledge Base (KB) entities and tabular data. Our
approach obtains state-of-the-art performance on a majority of cases across 15
datasets. In addition, experiments with a device KB and a nuclear physics
dataset demonstrate the model's ability to learn representations useful for
entity completion in diverse settings. This has many downstream use cases,
including modeling numerical properties with high accuracy - critical for
science applications, which also benefit from the model's inherent
probabilistic nature.

我们开发了一种生成式基于注意力的方法，用于对结构化实体进行建模，这些实体包括不同类型的属性，如数值、分类、字符串和组合。我们的方法可以处理这种异构数据，并通过连续 - 离散的扩散过程来对属性进行建模。我们的灵活框架可以对具有任意层次属性的实体进行建模，从而能够应用于结构化知识库实体和表格数据。我们的方法在 15 个数据集中的大多数情况下都获得了最先进的性能。此外，通过设备知识库和核物理数据集的实验，证明了该模型在各种环境中学习有用的实体补全表示的能力。这对模型的固有概率特性来说非常重要，在科学应用中对数值属性进行高准确性建模也有很大好处。