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