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A scalable and adaptive method for finding semantically equivalent cue words of uncertainty

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  • Chen, Chaomei
  • Song, Min
  • Heo, Go Eun

Abstract

Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists’ domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertainty types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts’ unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words is verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.

Suggested Citation

  • Chen, Chaomei & Song, Min & Heo, Go Eun, 2018. "A scalable and adaptive method for finding semantically equivalent cue words of uncertainty," Journal of Informetrics, Elsevier, vol. 12(1), pages 158-180.
  • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:158-180
    DOI: 10.1016/j.joi.2017.12.004
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Chaomei Chen & Zhigang Hu & Jared Milbank & Timothy Schultz, 2013. "A visual analytic study of retracted articles in scientific literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 234-253, February.
    3. Noa P. Cruz & Maite Taboada & Ruslan Mitkov, 2016. "A machine-learning approach to negation and speculation detection for sentiment analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(9), pages 2118-2136, September.
    4. Chaomei Chen & Zhigang Hu & Jared Milbank & Timothy Schultz, 2013. "A visual analytic study of retracted articles in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 234-253, February.
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    Cited by:

    1. Xiaoying Li & Suyuan Peng & Jian Du, 2021. "Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6225-6251, July.
    2. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    3. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.
    4. Yinghua Xie & Dong Lang & Shuna Lin & Fangfei Chen & Xiaodong Sang & Peng Gu & Ruijun Wu & Zhifei Li & Xuan Zhu & Lu Ji, 2021. "Mapping Maternal Health in the New Media Environment: A Scientometric Analysis," IJERPH, MDPI, vol. 18(24), pages 1-16, December.
    5. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Yoon, Jeeyoung & Syafiandini, Arida Ferti & Song, Min, 2023. "Exploring the knowledge certainty shift: Metaknowledge analysis on drugs via assertion uncertainty burstiness," Journal of Informetrics, Elsevier, vol. 17(2).
    7. Lutz Bornmann & K. Brad Wray & Robin Haunschild, 2020. "Citation concept analysis (CCA): a new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. K," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1051-1074, February.

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