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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Predictive analysis of multiple future scientific impacts by embedding a heterogeneous network.

Masanao Ochi1, Masanori Shiro2, Jun'ichiro Mori3

  • 1Department of Technology Management for Innovation, The University of Tokyo, Bunkyo, Tokyo, Japan.

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Predicting future research impact is crucial for investment. This study integrates multiple models using network-based learning to capture trends, improving predictions of scientific indices like the author h-index and journal impact factor.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Artificial Intelligence

Background:

  • Accurate prediction of research impact is vital for strategic investment decisions.
  • Increasing digital publications and research fragmentation necessitate automated methods for trend identification.
  • Previous methods often used index-specific features, failing to capture holistic research trends.

Purpose of the Study:

  • To demonstrate the feasibility of integrating multiple prediction models for scientific indices.
  • To leverage network-based representation learning for capturing and expressing scientific trends.
  • To develop a more comprehensive approach to predicting future research impacts.

Main Methods:

  • Utilized deep learning to integrate diverse individual models into general-purpose models.
  • Employed network embedding of a heterogeneous network representing scientific papers.
  • Conducted predictive analysis of multiple scientific indices, including author h-index, journal impact factor (JIF), and Nature Index.

Main Results:

  • The proposed multiple heterogeneous network embedding method improved performance by 1.6 points compared to single citation network embedding.
  • Achieved superior prediction results over baseline methods for multiple indices.
  • Successfully predicted scientific impacts such as author h-index, JIF, and Nature Index three years post-publication.

Conclusions:

  • Network embedding of heterogeneous scientific networks provides a robust basis for automatic prediction of research trends.
  • Integrated network-based representation learning offers a promising avenue for capturing multifaceted scientific dynamics.
  • This approach enhances the accuracy and scope of predicting future research influence.