基于B-CNN模型的异构网络大数据知识扩充算法研究  

Research on knowledge expansion algorithm of heterogeneous network big data based on B-CNN model

作　　者：张伟华 王海英 ZHANG Weihua;WANG Haiying(College Mechanical and Electrical Engineering, Zhengzhou Business University, Gongyi 451200, China)

机构地区：[1]郑州商学院信息与机电工程学院,河南巩义451200

出　　处：《兵器装备工程学报》2022年第6期290-294,共5页Journal of Ordnance Equipment Engineering

基　　金：河南省科技攻关计划项目(202102210357);郑州商学院新工科创新融合团队项目(2021-CXTD-05)。

摘　　要：在B-CNN模型各个特征通道内引进比例因子,结合正则化激活方式构建稀疏层,完成特征通道筛选,利用改进B-CNN构建异构网络大数据知识表示模型,通过维度变换方式增加卷积滑动窗口的滑动步数,提高数据内实体与关系的信息共享作用,利用可变粒度策略分割有效数据知识三元组的细粒度数据,实现异构网络大数据知识扩充。实验证明:该算法在表示数据知识时三元组预测准确比例较高,归一化互信息与调整兰德指数均较高,收敛速度较快,数据知识表示效果和扩充效果较好。The scale factor was introduced into each characteristic channel of B-CNN,the sparse layer was constructed in combination with the regularization activation method,the characteristic channel screening was completed,the heterogeneous network big data knowledge representation model was constructed by using the improved B-CNN,the sliding steps of convolution sliding window were increased by dimension transformation,and the information sharing function of entities and relationships in the data was improved.The variable granularity strategy was used to segment the fine-grained data of effective data knowledge triples to realize the knowledge expansion of big data in heterogeneous networks.Experiments show that the algorithm has a high proportion of triple prediction accuracy,high normalized mutual information and adjusted Rand index,fast convergence speed,and good data knowledge representation and expansion effect.

关 键 词：B-CNN模型 异构网络 大数据 知识扩充 比例因子 可变粒度 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]