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Updated: Jun 26, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

A new evaluation methodology for literature-based discovery systems.

Meliha Yetisgen-Yildiz¹, Wanda Pratt

¹Kiha, Inc., 100 S. King Street, Suite 320, Seattle, WA 98104, USA. meliha@kiha.com

Journal of Biomedical Informatics

|January 7, 2009

Summary

Researchers need better ways to find connections in medical literature. This study introduces a new evaluation method for literature-based discovery (LBD) systems to compare their performance and improve knowledge discovery.

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Last Updated: Jun 26, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Area of Science:

Biomedical Informatics
Medical Research
Information Science

Background:

The increasing volume of medical literature hinders researchers' ability to identify cross-disciplinary connections for hypothesis generation.
Literature-based discovery (LBD) systems aim to bridge knowledge gaps by mining connections from text.
Existing LBD systems employ diverse methods for connection mining and ranking, but lack standardized evaluation approaches.

Purpose of the Study:

To present a novel evaluation methodology for literature-based discovery (LBD) systems.
To enable direct comparison of the effectiveness of different LBD system approaches.
To facilitate informed algorithm selection and enhance overall LBD system performance.

Main Methods:

Developed a standardized evaluation methodology for LBD systems.
Applied the methodology to compare correlation-mining and ranking approaches within existing LBD systems.
Focused on assessing the performance of different connection-mining and ranking strategies.

Main Results:

Demonstrated the utility of the proposed evaluation methodology through comparative analysis.
Highlighted performance differences between various correlation-mining and ranking techniques.
Provided a framework for assessing LBD system effectiveness.

Conclusions:

The developed evaluation methodology allows for effective comparison across different LBD systems.
This framework will aid researchers in selecting optimal algorithms for knowledge discovery.
Implementing this methodology can lead to significant improvements in LBD system performance and medical research.