摘要
知识单元的特征信息是其学科归属判定的基础,挖掘关键特征有助于提升学科判定方法的性能,从而更好地服务于知识内容层面的跨学科规律研究。本文借助16种知识单元学科归属判定方法,通过对比分析这些方法,判别不同词频、不同学科覆盖度词汇的学科归属,评价方法所蕴含的学科重要度、学科相关度和学科区分度3种特征和特征组合效果,以挖掘效果最好的特征子集。本文以“计算医学”这一交叉领域数据为基础构建测试数据集,研究分析表明,综合使用3种特征的方法在各组数据上均取得了较好的性能,同时学科重要度的性能优势表明其在3种特征中最为重要;高频词的学科归属判定需要注重学科区分度,而低频词需要重点考虑学科重要性;对多学科覆盖度的知识单元,需要在学科重要度基础上补充对学科区分度的考虑。本文的发现能够为知识单元学科归属判定方法优化提供理论指导和实践建议。
The features of knowledge units are the basis of the discipline identification of knowledge units.Mining the key features of knowledge units can help improve performance,so as to better serve the study of interdisciplinary research at the knowledge content level.In this study,with the help of 16 methods of discipline identification for knowledge units,we compared and analyzed the discriminative performance of these methods for knowledge units with different word frequencies and disciplinary coverages.Further,we evaluated the effect of the three features and feature combinations of disciplinary importance,disciplinary relevance,and disciplinary discriminability implied by the methods to mine the subset of features with the best effect.We also constructed a test dataset based on data from the cross-cutting field of“computational medicine.”The experimental analysis results showed that the combined use of the three features achieved better performance on all groups,while the performance advantage of disciplinary importance indicates that it is the most important among the three features;the discipline identification of high-frequency words needs to focus on disciplinary importance,while low-frequency words need to focus on disciplinary importance.For knowledge units with multidisciplinary coverage,it is necessary to consider disciplinary differentiation in addition to disciplinary importance.The findings of this study provide theoretical guidance and practical suggestions for the optimization of discipline identification methods for knowledge units.
作者
操玉杰
向荣荣
毛进
王施运
Cao Yujie;Xiang Rongrong;Mao Jin;Wang Shiyun(School of Information Management,Central China Normal University,Wuhan 430079;School of Information Management,Wuhan University,Wuhan 430072)
出处
《情报学报》
CSSCI
CSCD
北大核心
2023年第10期1151-1165,共15页
Journal of the China Society for Scientific and Technical Information
基金
国家社会科学基金项目“词汇范式功能视角下交叉领域知识生长机制研究”(20CTQ024)。
关键词
知识单元
学科归属
知识分类
交叉学科
知识计量
knowledge unit
discipline identification
knowledge classification
interdisciplinary science
knowledge metrics
