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换为连续、低维的空间，以促进推理和补全任务。现有方法主要分为两类：平移距离模型和语义匹配模型。平移距离模型面临的一个关键挑战是难以有效区分图中的"头"实体和"尾"实体。为解决该问题，研究者提出了一种新型位置敏感嵌入（LSE）方法。LSE创新性地利用关系特定映射来修正头实体，将关系概念化为线性变换而非简单平移。本文深入探讨了LSE的理论基础，包括其表示能力及与现有模型的关联性。此外，还提出了一种更精简的变体LSE-d，采用对角矩阵进行变换以提高实际效率。在四个大规模知识图谱数据集上进行的链接预测实验表明，LSE-d在性能上优于或与现有最优方法相当。