Large-scale Entity Alignment in Knowledge Graphs Using Language Models

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.