Table_2_Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm.XLSX

The literature on neuroscience has grown rapidly in recent years with the emergence of new domains of research. In the context of this progress, creating a knowledge organization system (KOS) that can quickly incorporate terms of a given domain is an important aim in the area. In this article, we develop a systematic method based on word representation and the agglomerative clustering algorithm to semi-automatically build a hierarchical KOS. We collected 35,832 research keywords and 11,497 research methods from PubMed Central database, and organized them in a hierarchical structure according to semantic distance. We show that the proposed KOS can help find terms related to the given topics, analyze articles related to specific domains of research, and characterize the features of article clusters. The proposed method can significantly reduce the manual work required by experts to organize the KOS.

近年来，神经科学领域的研究文献迅速增长，涌现出新的研究领域。在此进展的背景下，构建一个能够快速整合特定领域术语的知识组织系统（KOS）成为该领域的重要目标。在本文中，我们基于词表示和聚合聚类算法，开发了一种系统性的方法，以半自动的方式构建一个层次化的KOS。我们从PubMed Central数据库中收集了35,832个研究关键词和11,497个研究方法，并按照语义距离将它们组织成一个层次结构。我们表明，所提出的KOS可以帮助找到与给定主题相关的术语，分析与特定研究领域相关的文章，并表征文章集群的特征。所提出的方法可以显著减少专家在组织KOS时所需的手动工作。