基于属性融合的多真值发现方法  

Multi-Truth Discovery Method Based on Attribute Fusion

作　　者：杨昊霖 董永权[1,2] 陈华凤 张国玺 Yang Haolin;Dong Yongquan;Chen Huafeng;Zhang Guoxi(School of Computer Science and Technology,Jiangsu Normal University,Xuzhou 221008,China;Xuzhou Engineering Research Center of Cloud Computing,Xuzhou 221100,China)

机构地区：[1]江苏师范大学计算机科学与技术学院,徐州221008 [2]徐州市云计算工程技术研究中心,徐州221100

出　　处：《数据分析与知识发现》2022年第11期52-60,共9页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:61872168);江苏师范大学研究生科研创新项目(项目编号:2021XKT1381)的研究成果之一。

摘　　要：【目的】解决现有方法多数只侧重于多真值属性自身,缺少考虑辅助属性影响的问题,提高多真值发现的效果。【方法】利用辅助属性计算数据源专业度和共识度,结合多真值属性值的活跃度得到数据源对冲突数据的支持度。通过调用已有真值发现方法获取真值伪标签,使用神经网络捕获数据源和冲突数据的复杂关系,最终推理出全部真值。【结果】实验结果表明,与次优方法相比,在图书数据集上F1值提升2.25%,在电影数据集上F1值提升5.42%。【局限】所提方法融合了反映对象特征的辅助属性,尚未探索其余辅助属性对多真值发现的影响。【结论】基于多真值属性与辅助属性融合的方法提高了多真值发现的准确性。[Objective]This paper adds influence of auxiliary attributes to the existing models for multi-truth discovery,aiming to improve their F1 values.[Methods]First,we used the auxiliary attributes to calculate the source expertise and consensus degree.Then,we combined the activity degree of multi-truth attribute values to get the degree of support from the source for the conflicting data.Third,we called the existing truth discovery methods to obtain the pseudo tags of the truth.Finally,we used the neural network to capture the complex relationship between the sources and the conflicting data,and identified all truth.[Results]Compared with the sub-optimal model,our method improved the F1 value by 2.25%on the book dataset and by 5.42%on the movie dataset.[Limitations]The proposed method included auxiliary attributes reflecting object features,and more research is needed to explore the impacts of other auxiliary attributes on multi-truth discovery.[Conclusions]The proposed method could effectively discover multi-truth.

关 键 词：多真值发现 数据冲突 信息质量 多真值属性 辅助属性 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]