计算机工程与应用2024，Vol.60Issue(15)：111-121,11.DOI:10.3778/j.issn.1002-8331.2304-0260
融合图像信息的多嵌入表示实体对齐方法
Multi-Embedding Representation Entity Alignment Method Based on Image Fusion Information
摘要
Abstract
Entity alignment is the key step of knowledge graph fusion technology.However,existing methods fail to make full use of graph data when processing cross-language graph.Hence,the paper proposes a multi-embedding repre-sentation entity alignment method based on image fusion.This method obtains text embeddings from different angles,enriches text embeddings with image data,and realizes multi-modal information fusion to complete entity alignment across linguistic knowledge graph.The image generation model is used to solve the problem of incomplete entity image coverage,and the high-quality entity image information is obtained by the iterative strategy to expand the seed sequence pairs in the cross-language knowledge graph.In order to better apply the knowledge graph fusion process in the real world,the method transforms the alignment phase into a binary graph matching problem.The proposed method is experi-mentally analyzed on a public data set,and the experimental results show the good performance of the method.The ablation experiment also verifies the effectiveness of each module,and provides the parameter selectivity for different situations.关键词
实体对齐/知识图谱/知识融合/跨语言Key words
entity alignment/knowledge graph/knowledge fusion/cross-language分类
信息技术与安全科学引用本文复制引用
刘春梅,高永彬,余文俊..融合图像信息的多嵌入表示实体对齐方法[J].计算机工程与应用,2024,60(15):111-121,11.基金项目
科技创新2030—"新一代人工智能"重大项目(2020AAA0109300) （2020AAA0109300）
上海市科委科技创新行动计划项目(21DZ1204900) （21DZ1204900）
上海市地方能力建设项目(21010501500). （21010501500）
