Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing methods generate aligning entity representation by mining the relevance of triple elements via embedding-based methods, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above issues considering ensemble triple specificity and entity role features. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic space and type space, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, our framework uses triple-ware entity enhancement to model the role diversity of triple elements. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework outperforms state-of-the-art methods.

翻译：实体对齐（Entity Alignment, EA）是融合跨语言及跨领域知识图谱（Knowledge Graphs, KGs）的关键任务，旨在从不同图中识别指向同一真实世界对象的实体。现有方法多通过基于嵌入的方式挖掘三元组元素关联性以生成对齐实体表示，但较少关注三元组不可分割性及实体角色多样性。本文提出新型框架TTEA——基于三元组感知注意力的类型增强集成三元组表示跨语言实体对齐方法，通过融合集成三元组特异性和实体角色特征解决上述问题。具体而言，将关系视为语义空间与类型空间之间的信息载体，从而通过特异性感知三元组注意力平滑控制空间变换与信息传播中的噪声影响。此外，本框架采用三元组感知实体增强机制建模三元组元素角色多样性。在三个真实跨语言数据集上的广泛实验表明，本框架性能超越现有最优方法。