Research Progress and Hotspot of Information Retrieval Correlation based on CiteSpace

J. Chen
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引用次数: 0

Abstract

Taking 1760 journal papers of information retrieval relevance in the core database of Web of Science as the research object, using literature co-citation network and keyword co-occurrence network analysis, with information visualization as the means, this paper summarizes the research on the relevance of information retrieval. It is found that the current information retrieval correlation research network is concentrated, which is mainly divided into two main knowledge groups, retrieval algorithm and correlation cognition, with few frontier branches. And the knowledge fusion between the two groups needs to be strengthened. Since the emergence of natural language processing, information retrieval relevance has been improved along the path of natural language processing-machine learning-deep learning.
基于CiteSpace的信息检索关联研究进展与热点
本文以Web of Science核心数据库中1760篇信息检索相关性期刊论文为研究对象,采用文献共引网络和关键词共现网络分析,以信息可视化为手段,对信息检索相关性的研究进行了总结。研究发现,当前信息检索相关研究网络较为集中,主要分为检索算法和关联认知两大知识群,前沿分支较少。两组之间的知识融合有待加强。自自然语言处理出现以来,信息检索相关性沿着自然语言处理-机器学习-深度学习的路径不断得到提升。
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