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2025 , Vol. 69 >Issue 7: 42 - 52

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.07.004

RESEARCH PAPERS

Predicting Scholar’s Influence Based on the Novelty Indicators of Early Knowledge Structure: A Case Study in the Biomedical Field

  • Wu Zhixiang ,
  • Yi Haigang ,
  • Zhu Yimeng ,
  • Wang Pei ,
  • Wang Hao
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  • 1 School of Economics and Management, Nanjing Tech University, Nanjing 211816;
    2 School of Information Management, Nanjing University, Nanjing 211023
Wu Zhixiang,associate professor,PhD,master supervisor,E-mail:cnwzx2012@njtech.edu.cn;Yi Haigang,master candidate;Zhu Yimeng,master candidate;Wang Pei,master candidate;Wang Hao,professor,PhD,doctoral supervisor.

Received date: 2024-06-25

  Revised date: 2024-09-21

  Online published: 2025-03-24

Supported by

This work is supported by the National Natural Science Foundation of China project titled “Research on Key Core Technologies-Experts Combination Model Based on Deep Learning and Semantic Association” (Grant No. 7190408) and the National Social Science Fund of China project titled “Research on Knowledge Association and Value Mining of Documentary Resources of Chinese Meritorious Scientists” (Grant No. 23CTQ027).

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Abstract

[Purpose/Significance] This study designs indicators to measure the novelty of a scholar’s early knowledge structure based on the innovative characteristics inherent in it. The purpose is to predict the scholar’s future influence, thereby providing new indicators for the early identification of academic talents. [Method/Process] Firstly, a dataset of 59,207 scholars in the biomedical field was obtained from the PubMed knowledge graph (PKG) database, and the knowledge structure of scholars was constructed using the co-occurrence relationships of controlled subject terms. Secondly, 6 indicators were designed to measure the novelty of scholars’ early knowledge structures on two levels: knowledge units and structural positions. Then, scholars were classified and labeled based on their later influence, and some machine learning models were trained. Finally, the classification effectiveness of different models was evaluated using the combination variable method, and the predictive performance of indicators based on the novelty of knowledge structures was analyzed. [Result/Conclusion] The study finds that novelty indicators can effectively predict influence. Among single-indicator predictions, topic novelty (TN) performs the best, while the 4 structural indicators all outperform the topic combination novelty (TCN). The F1 value of the comprehensive indicators is improved by an average of 2.7%. The novelty of knowledge structures provides a perspective for predicting and understanding scholars’ academic influence from content characteristics. The indicators designed are valuable and can complement existing prediction indicators.

Key words： influence prediction; knowledge structure of scholar; novelty indicators; machine learning

Cite this article

Wu Zhixiang , Yi Haigang , Zhu Yimeng , Wang Pei , Wang Hao . Predicting Scholar’s Influence Based on the Novelty Indicators of Early Knowledge Structure: A Case Study in the Biomedical Field[J]. Library and Information Service, 2025 , 69(7) : 42 -52 . DOI: 10.13266/j.issn.0252-3116.2025.07.004

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