  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.
