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.

本研究介绍了数据预处理在机器学习中的重要性，并提出了一种融合不同输入数据类型和格式的现代数据处理技术，重点介绍了命题化和嵌入式数据转换方法，并提出了将两种方法统一的框架和方法，这有助于解决复杂的数据转换和学习任务。通过两种高效的实现方法，实验证明了该方法的有效性和优越性。

命题化与嵌入：等价的两面硬币

Propositionalization and Embeddings: Two Sides of the Same Coin

Methods based on representation learning currently hold the state-of-the-art
in many natural language processing and knowledge base inference tasks. Yet, a
major challenge is how to efficiently incorporate commonsense knowledge into
such models. A recent approach regularizes relation and entity representations
by propositionalization of first-order logic rules. However,
propositionalization does not scale beyond domains with only few entities and
rules. In this paper we present a highly efficient method for incorporating
implication rules into distributed representations for automated knowledge base
construction. We map entity-tuple embeddings into an approximately Boolean
space and encourage a partial ordering over relation embeddings based on
implication rules mined from WordNet. Surprisingly, we find that the strong
restriction of the entity-tuple embedding space does not hurt the
expressiveness of the model and even acts as a regularizer that improves
generalization. By incorporating few commonsense rules, we achieve an increase
of 2 percentage points mean average precision over a matrix factorization
baseline, while observing a negligible increase in runtime.

本文提出了一种高效的基于关联规则的方法，将实体元组嵌入映射到近似布尔空间中，通过从 WordNet 中进行的关联规则挖掘来鼓励关系嵌入上的部分排序，以实现自动化知识库的构建。通过引入少量的常识规则，相较于矩阵分解基准方法，实现平均精度的 2 个百分点的提高，并观察到运行时间几乎没有增加。