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<p>Interdisciplinary measurement quantifies the degree of knowledge integration within academic research and provides a basis for assessing knowledge diversity. Existing methodologies predominantly rely on citations and author collaborations, approaches that overlook textual content and require high-quality data. This paper presents a text-based method for measuring the interdisciplinary nature of research papers through keyword diversity. The approach imposes lower data-quality requirements while capturing knowledge diversity from a content perspective. First, keyword diversity datasets are constructed. Second, these data are used to measure paper interdisciplinarity. Metadata for papers published between 1900 and 2018 are collected from the Web of Science database. Two experiments are conducted: one based on reference lists and the other based on keywords. The controlled experiments evaluate integrated indices, including the Rao–Stirling index, True Diversity, and DIV*. The results demonstrate that the keyword-based method generally achieves higher recall than the reference-based method, which can substantially increase measurement error and operational complexity. In addition, the Rao–Stirling and True Diversity indices approximately follow a normal distribution, whereas DIV* exhibits monotonicity. A correlation analysis on large-scale statistical data distributions is also conducted, revealing significant correlations between the keyword-based and reference-based measures across the three indices (correlation coefficients of 0.97, 0.90, and 0.74, respectively). These results confirm the feasibility and effectiveness of the keyword-based approach.</p>

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Interdisciplinarity in academic research: a keyword-based approach to knowledge diversity

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Jiajia Sun,
  • Yajing Li

摘要

Interdisciplinary measurement quantifies the degree of knowledge integration within academic research and provides a basis for assessing knowledge diversity. Existing methodologies predominantly rely on citations and author collaborations, approaches that overlook textual content and require high-quality data. This paper presents a text-based method for measuring the interdisciplinary nature of research papers through keyword diversity. The approach imposes lower data-quality requirements while capturing knowledge diversity from a content perspective. First, keyword diversity datasets are constructed. Second, these data are used to measure paper interdisciplinarity. Metadata for papers published between 1900 and 2018 are collected from the Web of Science database. Two experiments are conducted: one based on reference lists and the other based on keywords. The controlled experiments evaluate integrated indices, including the Rao–Stirling index, True Diversity, and DIV*. The results demonstrate that the keyword-based method generally achieves higher recall than the reference-based method, which can substantially increase measurement error and operational complexity. In addition, the Rao–Stirling and True Diversity indices approximately follow a normal distribution, whereas DIV* exhibits monotonicity. A correlation analysis on large-scale statistical data distributions is also conducted, revealing significant correlations between the keyword-based and reference-based measures across the three indices (correlation coefficients of 0.97, 0.90, and 0.74, respectively). These results confirm the feasibility and effectiveness of the keyword-based approach.