文章摘要
孙海霞,郝洁,郭臻,沈柳.基于知识元的细粒度医学量表文档知识表示框架构建[J].数字图书馆论坛,2023,19(12):86~98
基于知识元的细粒度医学量表文档知识表示框架构建
Construction of a Fine-Grained Knowledge Element-Based Framework for Knowledge Representation in Medical Scale Documents
投稿时间:2023-10-16  
DOI:10.3772/j.issn.1673-2286.2023.12.009
中文关键词: 量表文档;医学量表;知识元;知识组织;知识表示;细粒度
英文关键词: Scale Document; Medical Scale; Knowledge Element; Knowledge Organization; Knowledge Representation; Fine-Granularity
基金项目:本研究得到国家社会科学基金项目“基于知识组织的量表资源语义互联研究”(编号:21BTQ069)、中国医学科学院医学与健康科技创新工程项目“医学知识管理与智能化知识服务关键技术研究”(编号:2021-I2M-1-056)、国家重点研发计划“多源信息融合的心肺功能 评测康复技术临床应用与评价”(编号:2022YFC3601005)资助。
作者单位
孙海霞 中国医学科学院/北京协和医学院医学信息研究所 
郝洁 中国医学科学院/北京协和医学院医学信息研究所 
郭臻 中国医学科学院/北京协和医学院医学信息研究所 
沈柳 中国医学科学院/北京协和医学院医学信息研究所 
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中文摘要:
      针对当前医学量表资源组织与服务粒度相对粗放问题,提出一种细粒度医学量表文档知识表示框架,促进医学量表资源的语义化组织与服务。以常用心理卫生评定量表为例,针对量表文档内容特征,基于知识元理论,分析量表文档蕴含的内容知识元、内容知识元集合及其语义关系,描述形成一种细粒度、层次化的量表文档知识表示框架。该框架包含1个量表实体知识元、6类核心功能修辞结构知识元和74个内容知识元,并定义其间存在的层级关系和8类相关关系。应用本框架进行医学量表资源知识表示与处理控制,可提升医学量表资源的语义化组织与服务能力。
英文摘要:
      To address the issue of relatively coarse granularity in the organization and service of medical scale resources, this study introduces a fine-grained framework for knowledge representation in medical scale documents, aimed at enhancing the semantic organization and service of these resources. Focusing on commonly used mental health assessment scales, this study employs knowledge element theory to examine the characteristics of scale documents. It analyzes the content knowledge elements within these scale documents, as well as the sets of these elements and their semantic relationships – among individual elements, between elements and their sets, and between sets themselves. This approach results in a fine-grained and hierarchical knowledge representation framework for scale documents. This framework comprises one scale entity knowledge element, six categories of core functional rhetorical structure knowledge elements, and 74 content knowledge elements. It also defines hierarchical relationships within the poposed framework, as well as 8 types of associations. Implementing this framework for the knowledge representation and processing of medical scale resources can significantly improve their semantic organization and service efficiency.
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