计算机应用研究2025，Vol.42Issue(1)：100-105,6.DOI:10.19734/j.issn.1001-3695.2024.05.0187
基于自适应融合技术的多模态实体对齐模型
Multi-modal entity alignment model based on adaptive fusion technology
摘要
Abstract
Multi-modal entity alignment aims to identify equivalent entities between different multi-modal knowledge graphs composed of structured triples and images associated with entities.The existing research on multi-modal entity alignment main-ly focuses on multi-modal fusion strategies,ignoring the problems of modal imbalance and difficulty in integrating different mo-dalities,and fails to fully utilize multi-modal information.To solve these problems,this paper proposed the MACEA model,this model used the multi-modal variational autoencoder method to actively complete the missing modal information,the dy-namic modal fusion method to integrate and complement the information of different modalities,and the inter-modal contrastive learning method to model the inter-modal relations.These methods effectively solve the problems of modal missing and the dif-ficulty in modal fusion.Compared with the baseline model,MACEA improves the hits@1 and MRR indicators by 5.72％and 6.78％,respectively.The experimental results show that the proposed method can effectively identify aligned entity pairs,with high accuracy and practicality.关键词
实体对齐/知识图谱/多模态/动态融合/模态缺失Key words
entity alignment/knowledge graph/multi-modal/dynamic fusion/modality missing分类
信息技术与安全科学引用本文复制引用
任楚岚,于振坤,关超,井立志..基于自适应融合技术的多模态实体对齐模型[J].计算机应用研究,2025,42(1):100-105,6.基金项目
辽宁省教育厅科学研究资助项目(LJKZ0449,LJKZ0434) （LJKZ0449,LJKZ0434）
