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迈向科学图谱中的多重本体。献给洛特·莱德斯多夫。

Towards multiple ontologies in science mapping. A tribute to Loet Leydesdorff.

作者信息

Rafols Ismael

机构信息

INGENIO (CSIC-UPV), Universitat Politècnica de València, València, Spain.

UNESCO Chair On Diversity and Inclusion in Global Science, Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, The Netherlands.

出版信息

Scientometrics. 2025;130(6):3229-3255. doi: 10.1007/s11192-025-05323-0. Epub 2025 Apr 25.

DOI:10.1007/s11192-025-05323-0
PMID:40852436
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12367962/
Abstract

This article reviews Loet Leydesdorff's contributions to science mapping. It explains how over the years, his mapping techniques evolved from journal mapping to global maps of science and finally towards interactive interfaces portraying multiple classifications and ontologies. It then critically reviews the challenges faced by current approaches to science mapping, which implicitly assume a 'natural' epistemic structure, with examples from two recent case studies. We observe that bottom-up algorithmic approaches, either based on citation or semantic approaches, lack conceptual consistency regarding the type of categories used: in a same classification a category captures methods, another one has materials, a third one contains empirical objects and a fourth is focused on theories, rather than having a single logic. I argue that science mapping would produce more useful representations by using ontologies based on a single logic that aligns with the particular conceptual needs of the analysis. Novel classification methods based on machine learning and language models hold promise to produce these tailored, question-driven ontologies.

摘要

本文回顾了洛特·莱德斯多夫对科学图谱的贡献。文中解释了多年来他的图谱绘制技术是如何从期刊图谱演变为全球科学图谱,最终发展为描绘多种分类和本体的交互式界面的。接着,本文批判性地审视了当前科学图谱方法所面临的挑战,这些方法隐含地假定了一种“自然”的认知结构,并列举了两个近期案例研究中的例子。我们观察到,无论是基于引用还是语义方法的自下而上的算法方法,在所用类别的类型方面都缺乏概念上的一致性:在同一分类中,一个类别涵盖方法,另一个包含材料,第三个包含经验对象,第四个专注于理论,而不是具有单一逻辑。我认为,通过使用基于与分析的特定概念需求相一致的单一逻辑的本体,科学图谱将产生更有用的表征。基于机器学习和语言模型的新型分类方法有望生成这些量身定制的、问题驱动的本体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/ed46917c755c/11192_2025_5323_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/c66b5ed138ce/11192_2025_5323_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/61a49b81dc09/11192_2025_5323_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/2570b4373515/11192_2025_5323_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/e92ccf68789f/11192_2025_5323_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/61a58e17fbd9/11192_2025_5323_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/fd59b1b8ca93/11192_2025_5323_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/9ea77ca4505b/11192_2025_5323_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/d93840327029/11192_2025_5323_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/ed46917c755c/11192_2025_5323_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/c66b5ed138ce/11192_2025_5323_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/61a49b81dc09/11192_2025_5323_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/2570b4373515/11192_2025_5323_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/e92ccf68789f/11192_2025_5323_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/61a58e17fbd9/11192_2025_5323_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/fd59b1b8ca93/11192_2025_5323_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/9ea77ca4505b/11192_2025_5323_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/d93840327029/11192_2025_5323_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abc/12367962/ed46917c755c/11192_2025_5323_Fig9_HTML.jpg

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