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Entity alignment(EA) is a crucial task for integrating cross-lingual andcross-domain knowledge graphs(KGs), which aims to discover entities referringto the same real-world object from different KGs. Most existing methodsgenerate aligning entity representation by mining the relevance of tripleelements via embedding-based methods, paying little attention to tripleindivisibility and entity role diversity. In this paper, a novel frameworknamed TTEA -- Type-enhanced Ensemble Triple Representation via Triple-awareAttention for Cross-lingual Entity Alignment is proposed to overcome the aboveissues considering ensemble triple specificity and entity role features.Specifically, the ensemble triple representation is derived by regardingrelation as information carrier between semantic space and type space, andhence the noise influence during spatial transformation and informationpropagation can be smoothly controlled via specificity-aware triple attention.Moreover, our framework uses triple-ware entity enhancement to model the rolediversity of triple elements. Extensive experiments on three real-worldcross-lingual datasets demonstrate that our framework outperformsstate-of-the-art methods.

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