基于深度学习的科学网机构名称规范化

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2024-07-14 DOI:10.3390/a17070312
Zijie Jia, Zhijian Fang, Huaxiong Zhang
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引用次数: 0

摘要

学术评价是对研究人员、机构或学科领域进行评估和衡量的过程。其目的是评估研究人员在学术界的贡献和影响,并确定他们在特定学科领域的声誉和地位。科学网(WOS)作为全球最著名的学术引文数据库,为学术评价提供了重要数据。然而,由于机构变更、翻译差异、数据库转录错误以及作者个人写作习惯等因素,WOS 文献中记录的机构名称存在歧义,进而影响到对研究人员和机构的科学评价。为了解决学术评价中的数据可靠性问题,本文提出了一种整合多粒度嵌入和多语境信息的 WOS 机构名称同义词识别框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Normalization of Web of Science Institution Names Based on Deep Learning
Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its goal is to evaluate their contributions and impact in the academic community, as well as to determine their reputation and status within specific disciplinary domains. Web of Science (WOS), being the most renowned global academic citation database, provides crucial data for academic evaluation. However, due to factors such as institutional changes, translation discrepancies, transcription errors in databases, and authors’ individual writing habits, there exist ambiguities in the institution names recorded in the WOS literature, which in turn affect the scientific evaluation of researchers and institutions. To address the issue of data reliability in academic evaluation, this paper proposes a WOS institution name synonym recognition framework that integrates multi-granular embeddings and multi-contextual information.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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