用环形扇区表示关系，提升知识图谱补全的语义建模能力。
Knowledge Graph Embeddings with Representing Relations as Annular Sectors
- 将关系建模为极坐标系中的环形扇区，结合模长与相位捕捉推理模式。
- 在FB15k-237、WN18RR和YAGO3-10上表现媲美主流模型。
- 适合关注关系语义结构与层级信息建模的研究者。
知识图谱（KG）作为实体与关系的多关系数据结构，在数据分析与推荐系统中至关重要。知识图谱补全（KGC），即链接预测，旨在推断缺失三元组（头实体，关系，尾实体），对下游应用极为关键。基于区域的嵌入模型通常将实体表示为点，关系表示为几何区域。然而，这些模型常忽略实体固有的语义层次结构。为此，我们提出SectorE，一种基于极坐标的新型嵌入模型。关系被建模为环形扇区，结合模长与相位以捕捉推理模式与关系属性；实体则嵌入于这些扇区内，直观编码层次结构。在FB15k-237、WN18RR和YAGO3-10上评估，SectorE表现竞争力，证明其在语义建模方面的优势。
原文摘要 · Abstract (English)
Knowledge graphs (KGs), structured as multi-relational data of entities and relations, are vital for tasks like data analysis and recommendation systems. Knowledge graph completion (KGC), or link prediction, addresses incompleteness of KGs by inferring missing triples (h, r, t). It is vital for downstream applications. Region-based embedding models usually embed entities as points and relations as geometric regions to accomplish the task. Despite progress, these models often overlook semantic hierarchies inherent in entities. To solve this problem, we propose SectorE, a novel embedding model in polar coordinates. Relations are modeled as annular sectors, combining modulus and phase to capture inference patterns and relation attributes. Entities are embedded as points within these sectors, intuitively encoding hierarchical structure. Evaluated on FB15k-237, WN18RR, and YAGO3-10, SectorE achieves competitive performance against various kinds of models, demonstrating strengths in semantic modeling capability.
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