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.

本文提出了一种名为 TTEA 的新框架，通过考虑集成三元组的特异性和实体角色特征来克服现有方法中对三元组不可分割性和实体角色多样性的关注不足，利用三元组感知注意力导出集成三元组表示，并使用三元组增强实体来建模三元组元素的角色多样性，在三个现实世界的跨语言数据集上展开的大量实验表明，本框架优于现有最先进的方法。

基于三元关注的类型增强集成三元表示用于跨语言实体对齐

Type-enhanced Ensemble Triple Representation via Triple-aware Attention  for Cross-lingual Entity Alignment

Cross-lingual and cross-domain knowledge alignment without sufficient
external resources is a fundamental and crucial task for fusing irregular data.
As the element-wise fusion process aiming to discover equivalent objects from
different knowledge graphs (KGs), entity alignment (EA) has been attracting
great interest from industry and academic research recent years. Most of
existing EA methods usually explore the correlation between entities and
relations through neighbor nodes, structural information and external
resources. However, the complex intrinsic interactions among triple elements
and role information are rarely modeled in these methods, which may lead to the
inadequate illustration for triple. In addition, external resources are usually
unavailable in some scenarios especially cross-lingual and cross-domain
applications, which reflects the little scalability of these methods. To tackle
the above insufficiency, a novel universal EA framework (OTIEA) based on
ontology pair and role enhancement mechanism via triple-aware attention is
proposed in this paper without introducing external resources. Specifically, an
ontology-enhanced triple encoder is designed via mining intrinsic correlations
and ontology pair information instead of independent elements. In addition, the
EA-oriented representations can be obtained in triple-aware entity decoder by
fusing role diversity. Finally, a bidirectional iterative alignment strategy is
deployed to expand seed entity pairs. The experimental results on three
real-world datasets show that our framework achieves a competitive performance
compared with baselines.

本文引入一种新型的通用实体对齐框架，该框架基于本体对与通过 triple-aware attention 实现的角色增强机制，而无需引入外部资源，实现了从不同知识图谱中发现等价对象的基本任务，该框架在三个真实数据集上的实验结果表明，与基线方法相比，具有竞争性能。