命题化与嵌入：同一枚硬币的两面
Propositionalization and Embeddings: Two Sides of the Same Coin
摘要：
数据预处理是机器学习流水线的重要组成部分，需要大量的时间和资源。预处理的一个组成部分是将数据转换为给定学习算法所需的格式。本文概述了关系学习中使用的一些现代数据处理技术，这些技术可以将不同类型和格式的输入数据融合为单个表数据表示，重点是命题化和嵌入数据转换方法。虽然这两种方法都旨在将数据转换为表格数据格式，但它们使用不同的术语和任务定义，被认为是针对不同的目标，并且在不同的上下文中使用。本文提供了一个统一的框架，允许这两个数据转换技术的统一定义，并通过解释这两种方法之间的相似性和差异，作为一个统一的复杂的数据转换任务的变体，以提高理解。除了统一的框架之外，本文的新颖之处在于结合了命题化和嵌入的统一方法，这得益于两者在解决复杂数据转换和学习任务方面的优势。我们提出了两个有效的实现统一的方法：一个基于实例的PropDRM方法，和一个基于特征的PropStar方法的数据转换和学习，以及他们的经验评估几个关系问题。结果表明，新的算法可以优于现有的关系学习器，可以解决更大的问题。
英文摘要：
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
