Topics and changing characteristics of knowledge organization research in the 21st century: a content analysis

Li Si, Yi He, Li Liu
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Abstract

PurposeKnowledge organization (KO) has been advancing at a progressively rapid pace under the influence of information technology. This study aims to explore the topics, characteristics, and trends of KO research in the 21st century.Design/methodology/approachThe full text of 4,360 KO-related articles published from 2000 to 2021 is collected. Through content analysis, this study identifies the topics, research methods, and application areas of each article, and the statistics are presented through a series of visualizations.FindingsIn total, 13 main topics, 105 sub-topics, 16 research methods, and 57 application areas are identified. Notably, classification has always been an important topic, while linked data, automated techniques, and ontology have become popular topics recently. Significant changing features have also occurred. The versatile use of research methods has increased, with empirical research becoming the mainstream. Application areas show a trend of refinement from subject areas to specific scenarios. Construction techniques present a combination of automated techniques, crowdsourcing, and experts.Originality/valueKO has evolved and diversified due to technological developments. This study is the first to focus on the continuous changing features over an extended, 21-year period, as opposed to sampling a few years. It also provides clues and insights for researchers and practitioners interested in KO to understand how it has changed in the Semantic Web and big data context.
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21世纪知识组织研究的主题与变化特征:内容分析
目的在信息技术的影响下,知识型组织正以越来越快的速度向前发展。本研究旨在探讨21世纪KO研究的主题、特点和趋势。设计/方法/方法收集2000年至2021年间发表的4360篇与ko相关的文章全文。本研究通过内容分析,明确每篇文章的主题、研究方法和应用领域,并通过一系列的可视化呈现统计数据。总共确定了13个主要主题,105个子主题,16种研究方法和57个应用领域。值得注意的是,分类一直是一个重要的话题,而关联数据、自动化技术和本体最近也成为热门话题。显著的变化特性也发生了。研究方法的多样化使用有所增加,实证研究成为主流。应用领域呈现出从学科领域向具体场景细化的趋势。建筑技术是自动化技术、众包和专家的结合。原创性/价值由于技术的发展,ko已经发展和多样化。这项研究是第一次将重点放在21年的持续变化特征上,而不是几年的抽样。它还为对KO感兴趣的研究人员和实践者提供了线索和见解,以了解它在语义网和大数据环境下的变化。
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