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Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities

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  • Shiyun Wang
  • Yaxue Ma
  • Jin Mao
  • Yun Bai
  • Zhentao Liang
  • Gang Li

Abstract

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity‐based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co‐occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co‐occurrences outperforms that based on MeSH terms and three earlier citation‐based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Suggested Citation

  • Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    • Handle: RePEc:bla:jinfst:v:74:y:2023:i:2:p:150-167
    DOI: 10.1002/asi.24719
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    References listed on IDEAS

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    Cited by:

    1. Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    2. Yuyan Jiang & Xueli Liu, 2023. "A construction and empirical research of the journal disruption index based on open citation data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 3935-3958, July.

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