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Large-scale Entity Alignment in Knowledge Graphs Using Language Models
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AI
DOI：10.3724/2096-7004.di.2025.0074.png)
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Abstract
En 中文
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.
Keywords:
Knowledge graph
Pre-trained language model
Entity alignment
Large-scale entity alignment
Dense retrieval