Abstract
AbstractAlgorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science‐Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.
Highlights
In recent years, scientometricians have developed methods for algorithmically constructing classifications of research publications based on relations between individual publications
We restrict the study to two aspects of class labeling: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms
We use two baseline classifications, one based on Medical Subject Headings (MeSH) and one based on Science-Metrix journal classification (SMJC), to evaluate two key aspects of different labeling approaches: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms
Summary
Scientometricians have developed methods for algorithmically constructing classifications of research publications based on relations between individual publications. This has been done using large publication sets of tens of millions of publications (Boyack & Klavans, 2014; Sjögårde & Ahlgren, 2018; Waltman & van Eck, 2012). The obtained classifications have been used for various applications, such as identification of research topics and specialties, normalization of citations, measuring interdisciplinarity, and mapping research fields (Ahlgren, Colliander, & Sjögårde, 2018; Milanez, Noyons, & de Faria, 2016; Ruiz-Castillo & Waltman, 2015; Sjögårde & Ahlgren, 2020; Small, Boyack, & Klavans, 2014; Šubelj, van Eck, & Waltman, 2016; Wang & Ahlgren, 2018). Hierarchical classifications with labeled classes make it possible for users to browse large document collections (Cutting, Karger, Pedersen, & Tukey, 1992; Seifert, Sabol, Kienreich, Lex, & Granitzer, 2014). Perianes-Rodriguez and Ruiz-Castillo (2017) point out that
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