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 entity alignment (EA) aims to find the equivalent entities
between crosslingual KGs, which is a crucial step for integrating KGs.
Recently, many GNN-based EA methods are proposed and show decent performance
improvements on several public datasets. Meanwhile, existing GNN-based EA
methods inevitably inherit poor interpretability and low efficiency from neural
networks. Motivated by the isomorphic assumption of GNNbased methods, we
successfully transform the cross-lingual EA problem into the assignment
problem. Based on this finding, we propose a frustratingly Simple but Effective
Unsupervised entity alignment method (SEU) without neural networks. Extensive
experiments show that our proposed unsupervised method even beats advanced
supervised methods across all public datasets and has high efficiency,
interpretability, and stability.

通过将跨语言实体对齐问题转化为分配问题，我们提出了一种无需神经网络的令人沮丧的简单但有效的无监督实体对齐方法 (SEU)，在所有公共数据集上甚至击败了先进的监督方法，并具有高效性、可解释性和稳定性。