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Summary

This study introduces a novel text mining approach combining citation analysis and topic modeling to build comprehensive knowledge graphs. The method enhances understanding of scientific history, particularly in Raman spectroscopy.

Keywords:

Data-driven Deep learning Nanoscience Nanostructure Raman

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Area of Science:

Scientific knowledge discovery
History of science
Bibliometrics

Background:

Text mining methods often yield incomplete knowledge graphs due to domain specificity.
Extracting hidden development patterns in scientific fields requires advanced analytical tools.

Purpose of the Study:

To develop a text mining method combining citation analysis and topic modeling for constructing comprehensive scientific knowledge graphs.
To reveal hidden development patterns in the history of science, using Raman spectroscopy as a case study.

Main Methods:

A novel method integrating citation analysis with topic modeling (Latent Dirichlet Allocation) was developed.
A rule-based tokenizer was designed to address challenges in chemical entity naming.
Performance was benchmarked against traditional text mining methods.

Main Results:

The proposed method significantly improved topic coherence (≥100% growth) and diversity (0–126% growth) compared to baseline models.
Effectiveness of the rule-based tokenizer in handling chemical nomenclature was demonstrated.
The knowledge graph successfully revealed topic distribution, relationships, and historical milestones in Raman spectroscopy.

Conclusions:

The integrated approach provides a powerful tool for science of science research.
This method offers new insights for historical surveys and development forecasting in research fields.
The approach is versatile for mapping scientific evolution and identifying key developments.