Embedding methods transform the knowledge graph into a continuous, low-dimensional space, facilitating inference and completion tasks. Existing methods are mainly divided into two types: translational distance models and semantic matching models. A key challenge in translational distance models is their inability to effectively differentiate between 'head' and 'tail' entities in graphs. To address this problem, a novel location-sensitive embedding (LSE) method has been developed. LSE innovatively modifies the head entity using relation-specific mappings, conceptualizing relations as linear transformations rather than mere translations. The theoretical foundations of LSE, including its representational capabilities and its connections to existing models, have been thoroughly examined. A more streamlined variant, LSE-d, which employs a diagonal matrix for transformations to enhance practical efficiency, is also proposed. Experiments conducted on four large-scale KG datasets for link prediction show that LSEd either outperforms or is competitive with state-of-the-art related works.

翻译：嵌入方法将知识图谱转化为连续的低维空间，便于推理和补全任务。现有方法主要分为两类：平移距离模型和语义匹配模型。平移距离模型的一个关键挑战是它们无法有效区分图中的“头”实体和“尾”实体。为解决这一问题，提出了一种新颖的位置敏感嵌入方法（LSE）。LSE创新性地通过关系特定映射来修改头实体，将关系概念化为线性变换而非简单的平移。本文深入研究了LSE的理论基础，包括其表征能力以及其与现有模型的联系。同时还提出了一种更为精简的变体LSE-d，该变体采用对角矩阵进行变换以提升实际效率。在四个大规模知识图谱数据集上针对链接预测进行的实验表明，LSEd的性能要么优于、要么与相关最先进工作持平。