摘要
面向大数据环境下的数据治理和名称规范建设,针对机构名称数据多样性和复杂性特征,尝试采用共现视角和异质网络挖掘方法,探究数据驱动的机构名称归一化,可提高文献网络构建、挖掘和应用质量。从共现视角的机构识别方法层面上,构建一级机构-二级机构-三级机构三重异质共现网络模型;将机构名称归一化问题转化为异质共现网络挖掘问题,构建基于元路径的机构名称归一化框架模型;系统化地设计基于元路径的拓扑特征和识别工具,通过异质共现网络的文本属性、地理属性和关系属性挖掘,识别隐性语义关系。以2008—2018年上海交通大学WoS(Web of Science)文献题录数据机构名称归一化为例,实验结果验证了该方法的有效性。
Facing data governance and name standardization in the big data environment and aiming at the diversity and complexity of institution name data,this paper attempts to use the co-occurrence perspective and the heterogeneous network mining method to explore the name normalization of data-driven institutions,which can improve the quality of document network construction,mining,and application.From the perspective of the co-occurrence institution identification method,a triple heterogeneous co-occurrence network model is constructed,which consists of a superior institution,an institution,and a subordinate institution.The normalization problem of the institution name is transformed into a heterogeneous co-occurrence network mining problem,and a meta-path-based framework model of institution name normalization is constructed.Topological features and recognition tools based on meta-path are systematically designed to identify any hidden semantic relationships by mining the text attributes,geographic attributes,and relationship attributes of the heterogeneous co-occurrence networks.Using the name normalization of WoS(Web of Science)bibliographic data institutions in Shanghai Jiaotong University from 2008 to 2018 as an example,the experimental results verify the effectiveness of the method.
作者
杨昭
Yang Zhao(Shanghai Jiao Tong University Library,Shanghai 200240)
出处
《情报学报》
CSSCI
CSCD
北大核心
2020年第10期1069-1080,共12页
Journal of the China Society for Scientific and Technical Information
基金
教育部人文社会科学研究青年基金项目“基于在线评论情感分析的图书销量预测研究”(19YJCZH130)。
关键词
机构名称
归一化
异质共现网络
元路径
大数据
institution name
normalization
heterogeneous co-occurrence network
meta-path
big data
