农业机械学报2025，Vol.56Issue(1)：56-64,9.DOI:10.6041/j.issn.1000-1298.2025.01.006
基于"图像-文本"间关联增强的多模态猪病知识图谱融合方法
"Image-Text"Association Enhanced Multi-modal Swine Disease Knowledge Graph Fusion
摘要
Abstract
Traditional swine disease prevention primarily relies on human expertise,which risks missed diagnoses due to human error.To address this challenge,a multi-modal swine disease knowledge graph was developed to assist managers in better understanding the connections between pigs,providing a solid data foundation for identifying potential disease transmission paths and anomalies.Firstly,the swine disease data from various sources were collected,and then two preliminary multi-modal knowledge graphs were constructed through knowledge extraction and image matching.Secondly,a multi-modal knowledge graph fusion method based on"image-text"association was proposed,using a multi-head attention mechanism to reduce the impact of visual ambiguity and enhance swine disease entity representation.Finally,by calculating the similarity of entity representations in vector space,entities from the two multi-modal datasets were integrated into a more comprehensive knowledge graph.Experiments demonstrated that the proposed method improved alignment accuracy,as reflected by a 0.033 increase in Hits@1 compared with that of existing methods.Additional accuracy gains of 0.152,0.236 and 0.180 were observed on the DBPZH-EN,DBPFR-EN,and DBPJA-EN datasets respectively,demonstrating its effectiveness in multi-modal knowledge graph fusion.关键词
猪病/多模态知识图谱/多模态融合/实体对齐Key words
swine disease/multi-modal knowledge graph/multi-modal fusion/entity alignment分类
计算机与自动化引用本文复制引用
蒋婷婷,徐澳,吴飞飞,杨帅,何进,辜丽川..基于"图像-文本"间关联增强的多模态猪病知识图谱融合方法[J].农业机械学报,2025,56(1):56-64,9.基金项目
国家自然科学基金面上项目(32472007)和安徽省高等学校科学研究项目(自然科学类)重点项目(2023AH051020) （32472007）
