DensE：一种增强的非交换自适应语义层次知识图谱嵌入表示
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy
摘要：
获取关系的组成模式是知识图谱构建中的一项重要任务。它也是对学习知识进行多跳推理的基本步骤。以前，已经开发了几种基于旋转的平移方法，使用一系列复值对角矩阵的乘积来建模复合关系。然而，这些方法倾向于对复合关系做出几个过于简化的假设，例如，迫使它们是可交换的、独立于实体并且缺乏语义层次。为了系统地解决这些问题，我们已经开发了一种新的知识图谱嵌入方法，命名为DensE，提供了一个改进的建模方案的复杂组成模式的关系。特别地，我们的方法将每个关系分解为基于SO（3）群的旋转算子和三维（3-D）欧氏空间中的缩放算子。这种设计原则导致了我们的方法的几个优点：（1）对于复合关系，对应的对角关系矩阵可以是非交换的，反映了现实世界中的主要场景;（2）我们的模型保留了关系操作和实体嵌入之间的自然交互;（3）缩放操作为实体的内在语义层次结构提供了建模能力;（4）在参数大小和训练时间方面以高计算效率实现了DensE的增强的表达能力;（5）在欧几里得空间而不是四元数空间中建模实体保持了关系模式的直接几何解释。在多个基准知识图谱上的实验结果表明，DensE优于当前最先进的缺失链接预测模型，特别是在复合关系上。
英文摘要：
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.
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