Knowledge graph embedding transforms knowledge graphs into a continuous,
low-dimensional space, facilitating inference and completion tasks. This field
is mainly divided into 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, the 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, LSEd, employs a diagonal
matrix for transformations to enhance practical efficiency. In tests conducted
on four large-scale datasets for link prediction, LSEd either outperforms or is
competitive with leading contemporary models.

知识图谱嵌入主要分为平移距离模型和语义匹配模型，在平移距离模型中，头尾实体的区分能力是一个关键挑战，而新颖的位置敏感嵌入 (LSE) 方法通过使用关系特定的映射来改变头实体，将关系概念化为线性变换，而不是简单的平移，它的理论基础、表现能力和与现有模型的联系都得到了充分的研究，一个更简化的变体 LSEd 通过对变换使用对角线矩阵以提高实际效率，在四个大规模数据集上进行的链接预测测试中，LSEd 要么表现出色，要么与领先的现代模型竞争力相当。

知识图谱嵌入的位置敏感嵌入

Location Sensitive Embedding for Knowledge Graph Embedding

Semantic matching models -- which assume that entities with similar semantics
have similar embeddings -- have shown great power in knowledge graph embeddings
(KGE). Many existing semantic matching models use inner products in embedding
spaces to measure the plausibility of triples and quadruples in static and
temporal knowledge graphs. However, vectors that have the same inner products
with another vector can still be orthogonal to each other, which implies that
entities with similar semantics may have dissimilar embeddings. This property
of inner products significantly limits the performance of semantic matching
models. To address this challenge, we propose a novel regularizer -- namely,
DUality-induced RegulArizer (DURA) -- which effectively encourages the entities
with similar semantics to have similar embeddings. The major novelty of DURA is
based on the observation that, for an existing semantic matching KGE model
(primal), there is often another distance based KGE model (dual) closely
associated with it, which can be used as effective constraints for entity
embeddings. Experiments demonstrate that DURA consistently and significantly
improves the performance of state-of-the-art semantic matching models on both
static and temporal knowledge graph benchmarks.

本文提出了一种新型约束策略：由原始语义匹配 KGE 模型得到一个存在的与之关联的距离型 KGE 模型，将其用作实体嵌入的约束，使得语义相似的实体具有相似的嵌入，并在静态和时间知识图上显著提高了现有语义匹配模型的性能。