Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform KGC by learning embeddings. Nevertheless, most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. Additionally, the few available fine-grained semantic models rely on clustering algorithms, resulting in limited performance and applicability due to the cumbersome two-stage training process. In this paper, we present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner. More specifically, we represent each relation using a dictionary that contains multiple latent semantics. The composition of a given entity and the dictionary's central semantics serves as the context for generating a lookup, thus determining the fine-grained semantics of the relation adaptively. The proposed loss function optimizes both the central and fine-grained semantics simultaneously to ensure their semantic consistency. Besides, we introduce two metrics to assess the validity and accuracy of the dictionary lookup operation. We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.

翻译：知识图谱补全旨在通过预测已知三元组中的缺失链接来解决知识图谱的不完整性。目前已有大量知识图谱嵌入模型通过学习嵌入表示来实现知识图谱补全。然而，大多数现有嵌入模型将每个关系映射为单一向量，忽略了不同实体下关系所具有的细粒度语义。此外，少数可用的细粒度语义模型依赖聚类算法，由于繁琐的两阶段训练过程，导致其性能和适用性有限。本文提出一种利用上下文词典查找的新方法，使传统嵌入模型能够以端到端方式学习关系的细粒度语义。具体而言，我们使用包含多个潜在语义的词典表示每个关系，以给定实体与词典中心语义的组合作为上下文生成查找结果，从而自适应地确定关系的细粒度语义。所提出的损失函数同时优化中心语义与细粒度语义，以确保其语义一致性。此外，我们引入两个指标评估词典查找操作的有效性和准确性。我们将该方法扩展至多个知识图谱嵌入模型，在广泛使用的基准数据集上取得了显著的性能提升。