基于图形的作者的方法作者名字清晰化:一个调查调查
Michele De Bonis1, Fabrizio Falchi1, Paolo Manghi1,2
1Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" (ISTI), Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy.
PeerJ. Computer science
|October 9, 2023
概括
作者名称的明确性 (AND) 对于高质量的学术知识图 (SKG) 是至关重要的. 本文回顾了针对SKG量身定制的基于图形的AND方法,并提出了评估和未来研究方向的框架.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 图书统计学 图书统计学
背景情况:
- 学术知识图 (SKG) 对于研究发现和影响分析至关重要.
- 准确的作者名称分歧 (AND) 对于SKG内部连贯的研究人员个人资料至关重要.
- 现有的 AND 方法在大型 SKG 中面临同名性和数据稀疏性等挑战.
研究的目的:
- 在学术知识图 (SKG) 的背景下,专门解决作者名称清晰化 (AND) 方法.
- 探索如何利用 SKG 独特的实体关系结构来改善 AND.
- 为 SKG 中基于图形的 AND 技术提供全面的概述和评估框架.
主要方法:
- 审查和讨论最近的基于图形的方法,以作者的姓名清晰化 (AND).
- 定义一个标准化的框架来比较和评估不同的AND方法.
- 在SKGs中用于测试和方法的流行的数据集和基准的目录.
主要成果:
- 在SKGs的背景下,确定了传统AND方法的局限性.
- 通过利用SKG结构突出了基于图形的方法的潜力.
- 建立了 AND 方法的系统比较和基准测试的基础.
结论:
- 基于图表的方法为学术知识图表 (SKG) 中的作者名称清晰化 (AND) 提供了有希望的解决方案.
- 需要一个统一的框架来进行强有力的评估和推进AND技术.
- 未来的研究应该专注于利用SKG结构来实现更准确,更可扩展的AND.
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