Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several rotation-based translational methods have been developed to model composite relations using the product of a series of complex-valued diagonal matrices. However, these methods tend to make several oversimplified assumptions on the composite relations, e.g., forcing them to be commutative, independent from entities and lacking semantic hierarchy. To systematically tackle these problems, we have developed a novel knowledge graph embedding method, named DensE, to provide an improved modeling scheme for the complex composition patterns of relations. In particular, our method decomposes each relation into an SO(3) group-based rotation operator and a scaling operator in the three dimensional (3-D) Euclidean space. This design principle leads to several advantages of our method: (1) For composite relations, the corresponding diagonal relation matrices can be non-commutative, reflecting a predominant scenario in real world applications; (2) Our model preserves the natural interaction between relational operations and entity embeddings; (3) The scaling operation provides the modeling power for the intrinsic semantic hierarchical structure of entities; (4) The enhanced expressiveness of DensE is achieved with high computational efficiency in terms of both parameter size and training time; and (5) Modeling entities in Euclidean space instead of quaternion space keeps the direct geometrical interpretations of relational patterns. Experimental results on multiple benchmark knowledge graphs show that DensE outperforms the current state-of-the-art models for missing link prediction, especially on composite relations.
翻译:获取关系构成模式是知识图形完成过程中的一项至关重要的任务。 它还是朝着多点推理取多点推理而忽视所学知识的方向迈出的重要一步。 以前,已经开发了几种基于轮换的翻译方法,利用一系列复杂价值的对角矩阵的产物来模拟复合关系。 但是,这些方法往往在复合关系上作出一些过于简单的假设,例如,迫使它们具有通性、独立于实体和缺乏语义等级。为了系统地解决这些问题,我们开发了一个新的知识图表嵌入方法,名为 DensE, 以提供一个更完善的多重关系模式模式。特别是,我们的方法将每一种关系都分解成一个基于SO(3) 组的对角旋转操作器和一个三维(3-D) Euclidean 空间空间空间空间空间空间空间关系(3-D)的缩放操作。这个设计原则导致我们的方法有若干优点:(1) 对于复合关系,对应的对立面关系矩阵关系矩阵可能是非对等的,反映真实世界应用中的主要假设;(2) 我们的模型保存了关系运行运行中的关系和实体嵌嵌化当前模型;(3) 内部的Sqioral 计算系统结构提供了高的模型, 显示空间结构结构结构结构的升级的模型显示。