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Entity Alignment (EA) aims to find the equivalent entities between twoKnowledge Graphs (KGs). Existing methods usually encode the triples of entitiesas embeddings and learn to align the embeddings, which prevents the directinteraction between the original information of the cross-KG entities.Moreover, they encode the relational triples and attribute triples of an entityin heterogeneous embedding spaces, which prevents them from helping each other.In this paper, we transform both triples into unified textual sequences, andmodel the EA task as a bi-directional textual entailment task between thesequences of cross-KG entities. Specifically, we feed the sequences of twoentities simultaneously into a pre-trained language model (PLM) and propose twokinds of PLM-based entity aligners that model the entailment probabilitybetween sequences as the similarity between entities. Our approach captures theunified correlation pattern of two kinds of information between entities, andexplicitly models the fine-grained interaction between original entityinformation. The experiments on five cross-lingual EA datasets show that ourapproach outperforms the state-of-the-art EA methods and enables the mutualenhancement of the heterogeneous information. Codes are available athttps://github.com/OreOZhao/TEA.

From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment