摘要
针对现阶段档案检索查全率较低的现状,本文提出基于特征关联分析的档案信息关联模型。该模型根据档案特征特点将档案特征信息划分成多个视图,分别提取不同视图中档案信息的特征数据,并基于特征数据构建特征关联图,再将多视图下的档案特征关联图进行数据融合形成统一的档案信息关联模型。最后通过实验证明基于特征关联分析的档案信息关联模型能有效地将档案信息检索的查全率提高至82%,有助于档案系统智能检索的发展。
For the current situation where the archive retrieval rate is relatively low,this paper proposes a file information association model based on feature association analysis.The model divides the file feature information into multiple views according to the characteristics of the file features,extracts the feature data of the file information in different views,builds a feature association graph based on the feature data,and then combines the file feature association graphs in multiple views for data fusion.A unified file information association model.Finally,it is proved through experiments that the archival information association model based on feature association analysis can effectively improve the recall rate of archive information retrieval to82%,which contributes to the development of intelligent retrieval of archive systems.
作者
郭雪薇
董晶
GUO Xue-wei;DONG Jing(Dept.8of System,North China Institute of Computing Technology,Beijing 100083,China)
出处
《电子设计工程》
2019年第1期47-52,共6页
Electronic Design Engineering
基金
科技部国际合作交流重大专项资助(2015dfr50520)
关键词
关联模型
档案分析
特征关联
数据融合
association model
file analysis
feature correlation
data fusion
