Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (1): 271-277.doi: 10.11896/jsjkx.241100069

• Artificial Intelligence • Previous Articles     Next Articles

Cross-language Knowledge Graph Entity Alignment Based on Meta-learning

CHEN Zhuangzhuang1, DENG Yichen3, YU Dunhui1,2, XIAO Kui1,2   

  1. 1 School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China;
    2 Hubei Key Laboratory of Big Data Intelligent Analysis and Application, Wuhan 430062, China;
    3 Wuchang Shouyi College, Wuhan 430064, China
  • Received:2024-11-12 Revised:2025-02-11 Published:2026-01-08
  • About author:CHEN Zhuangzhuang,born in 1999,postgraduate.His main research interest is knowledge graph.
    DENG Yichen,born in 1993,master,lecturer.Her main research interest is embedded development.
  • Supported by:
    National Natural Science Foundation of China(62377009).

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Abstract: Cross-language knowledge graph entity alignment is a key step in connecting knowledge graphs of different languages,and it plays an important role in tasks such as multilingual information retrieval and data fusion.However,the existing entity alignment methods rely on a variety of information in the knowledge graph,which cannot handle the entity alignment task of the sparse knowledge graph well,and has poor adaptability to new languages.To solve this problem,a cross-language entity alignment framework based on meta-learning is proposed.The framework is generally divided into two stages,the outer loop and the inner loop.In the outer loop stage,multiple tasks are selected through the sampling method based on task similarity,and then the model is jointly trained with multiple tasks to construct the teacher model.In the inner loop stage,the teacher model trained in the outer loop stage is used to guide the student model to carry out the training and entity alignment tasks,in order to improve the entity alignment performance and generalization of the student model.Experimental results on the SRPRS and WK31-60K dataset show that the proposed framework improves the Hits@1 index by 3.5%,Hits@10 index by 4.0%,and MRR index by 6.3% on average in the entity alignment problem.

Key words: Meta-learning, Cross-language knowledge graph, Entity alignment, Outer loop, Inner loop, Generalization ability

CLC Number: 

  • TP391
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