Abstract
Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding of KGs, most of the current KG representation learning methods, where each entity is embedded as a vector in the Euclidean space and each relation is embedded as a transformation, follow an entity ranking protocol. On one hand, such an embedding design cannot capture many-to-many relations. On the other hand, in many retrieval cases, the users wish to get an exact set of answers without any ranking, especially when the results are expected to be precise, e.g., which genes cause an illness. Such scenarios are commonly referred to as "set retrieval". This work presents a pioneering study on the KG set retrieval problem. We show that the set retrieval highly depends on expressive modeling of many-to-many relations, and propose a new KG embedding model SpherE to address this problem. SpherE is based on rotational embedding methods, but each entity is embedded as a sphere instead of a vector. While inheriting the high interpretability of rotational-based models, our SpherE can more expressively model one-to-many, many-to-one, and many-to-many relations. Through extensive experiments, we show that our SpherE can well address the set retrieval problem while still having a good predictive ability to infer missing facts. The code is available at this https URL.
Abstract (translated)
知识图（KGs）作为一种存储大量关系事实（头,关系,尾）的数据结构，具有各种应用价值。尽管许多下游任务高度依赖于KGs的表示建模和预测嵌入，但目前大多数KG表示学习方法，其中每个实体以欧氏空间中的向量表示，每个关系以变换表示，都遵循实体排序协议。一方面，这种嵌入设计无法捕捉许多对多关系。另一方面，在许多检索案例中，用户希望获得一个无排名的准确集合答案，尤其是在结果预计精确的情况下，例如哪些基因导致疾病。这种情况通常被称为“集检索”。 本文在KG集检索问题上进行了一项开创性的研究。我们证明了集检索高度依赖于多对多关系的表示建模，并提出了一个新的KG嵌入模型SpherE来解决这个问题。SpherE基于旋转嵌入方法，但每个实体都被嵌入为一个球体而不是向量。虽然继承了旋转模型的高可解释性，但我们的SpherE可以更富有表现力地建模一对一、一对多和多对多关系。通过大量实验，我们证明了我们的SpherE可以在解决集检索问题的同时，仍具有推断缺失事实的良好预测能力。代码可在此处访问：https://www.acm.org/dl/d/2222216
URL
https://arxiv.org/abs/2404.19130
