Researcher Network Visualization Using Matrix Researcher2vec

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI:10.20965/jaciii.2023.p0603

Enna Hirata, Takahiro Yamashita, Seiichi Ozawa

{"title":"Researcher Network Visualization Using Matrix Researcher2vec","authors":"Enna Hirata, Takahiro Yamashita, Seiichi Ozawa","doi":"10.20965/jaciii.2023.p0603","DOIUrl":null,"url":null,"abstract":"In this study, we introduce a system called Matrix Researcher2vec (MResearcher2vec) which generates researcher embedding vectors from their papers and research projects in researchmap and KAKENHI databases. The system includes data on 276,841 researchers, 6,161,592 papers, and research projects. Utilizing natural language processing techniques, the MResearcher2vec model extracts researcher vectors from the papers and research project summaries of KAKENHI grant recipients. The similarity between reseachers is then computed to visualize inter-researcher relationships. The machine learning results have been integrated into a web service, providing a novel approach for academic relationship mining. It can be applied in the matching of research contents and researchers in evaluation of industry-government-academia collaboration and joint research. It contributes in four aspects: (1) exchanges between researchers, (2) creation of opportunities for researchers and companies to connect, (3) further promotion of interdisciplinary research, and (4) reduction of lost opportunities for research institutions to acquire talents.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"16 1","pages":"603-608"},"PeriodicalIF":0.7000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Computational Intelligence and Intelligent Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jaciii.2023.p0603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

In this study, we introduce a system called Matrix Researcher2vec (MResearcher2vec) which generates researcher embedding vectors from their papers and research projects in researchmap and KAKENHI databases. The system includes data on 276,841 researchers, 6,161,592 papers, and research projects. Utilizing natural language processing techniques, the MResearcher2vec model extracts researcher vectors from the papers and research project summaries of KAKENHI grant recipients. The similarity between reseachers is then computed to visualize inter-researcher relationships. The machine learning results have been integrated into a web service, providing a novel approach for academic relationship mining. It can be applied in the matching of research contents and researchers in evaluation of industry-government-academia collaboration and joint research. It contributes in four aspects: (1) exchanges between researchers, (2) creation of opportunities for researchers and companies to connect, (3) further promotion of interdisciplinary research, and (4) reduction of lost opportunities for research institutions to acquire talents.

查看原文本刊更多论文

基于矩阵的研究者网络可视化

在这项研究中，我们引入了一个名为Matrix Researcher2vec (MResearcher2vec)的系统，该系统从研究地图和KAKENHI数据库中的研究论文和研究项目中生成研究人员嵌入向量。该系统包括276841名研究人员、6161592篇论文和研究项目的数据。MResearcher2vec模型利用自然语言处理技术，从KAKENHI资助对象的论文和研究项目摘要中提取研究人员向量。然后计算研究人员之间的相似性，以可视化研究人员之间的关系。将机器学习结果集成到web服务中，为学术关系挖掘提供了一种新颖的方法。可用于产学研合作与联合研究的评价中研究内容与研究人员的匹配。它的贡献有四个方面:(1)研究人员之间的交流;(2)为研究人员和企业之间的联系创造机会;(3)进一步促进跨学科研究;(4)减少研究机构获得人才的机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Advanced Computational Intelligence and Intelligent Informatics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

1.50

自引率

14.30%

发文量

期刊介绍： JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.