  Entity Alignment (EA) aims to match equivalent entities in different
Knowledge Graphs (KGs), which is essential for knowledge fusion and
integration. Recently, embedding-based EA has attracted significant attention
and many approaches have been proposed. Early approaches primarily focus on
learning entity embeddings from the structural features of KGs, defined by
relation triples. Later methods incorporated entities' names and attributes as
auxiliary information to enhance embeddings for EA. However, these approaches
often used different techniques to encode structural and attribute information,
limiting their interaction and mutual enhancement. In this work, we propose a
dense entity retrieval framework for EA, leveraging language models to
uniformly encode various features of entities and facilitate nearest entity
search across KGs. Alignment candidates are first generated through entity
retrieval, which are subsequently reranked to determine the final alignments.
We conduct comprehensive experiments on both cross-lingual and monolingual EA
datasets, demonstrating that our approach achieves state-of-the-art performance
compared to existing EA methods.
