Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm - Huangfu, Cunqing; Zeng, Yi; Wang, Yuwei (Frontiers in Neuroinformatics, 2020)
How to navigate the ever-growing scientific literature database is a pressing problem. Knowledge Organization System (KOS) with hierarchical categorization, which can help us navigate through the literature. We have presented a systematical method to create such KOS which can effectively help us find sub-topics, provide search keywords, analyze articles related to specific research domains, and characterize the features of article clusters. The method is based on word representation and agglomerative clustering algorithm, which can significantly decrease the inevitable expert manual work required for the KOS organization. We have collected 35832 research keywords and 11497 research methods from PubMed Central XMLs.We have organized them into a hierarchal structure; all the hierarchical nodes have explainable specific meanings. Through two case studies, we demonstrated that the term system is versatile in bibliometric research and scientific literature retrieval.