摘要
Entity alignment(EA)is crucial for knowledge fusion and integration,as it aims to match equivalent entities across different KGs.Recently,many neural-based EA methods have been proposed,focusing on developing various graph representation learning models to match entities in vector spaces.However,most real-world KGs are large-scale and contain rich structural and attribute information about entities,presenting challenges for current approaches designed primarily for small-and medium-sized KGs.To address the challenges of large-scale EA,this paper introduces a simple,effective,and scalable method based on language models.Our approach first leverages the capabilities of language models to encode entities'multi-view information into low-dimensional embeddings,identifying potential aligned entity pairs with high similarity.These candidates are then re-ranked using a global matching algorithm to produce the final alignments.Experimental results show that our method achieves state-of-the-art performance on real-world large-scale EA datasets,with superior accuracy and efficiency compared to existing methods.
基金
supported by the National Natural Science Foundation of China(No.62276026)。
