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

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Graph-based methods for Author Name Disambiguation: a survey.

Michele De Bonis¹, Fabrizio Falchi¹, Paolo Manghi^1,2

¹Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" (ISTI), Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy.

Peerj. Computer Science

|October 9, 2023

Summary

This summary is machine-generated.

Author name disambiguation (AND) is crucial for high-quality scholarly knowledge graphs (SKGs). This paper reviews graph-based AND methods tailored for SKGs, proposing a framework for their evaluation and future research directions.

Keywords:

Author name disambiguation Deduplication Disambiguation

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Area of Science:

Computer Science
Information Science
Bibliometrics

Background:

Scholarly knowledge graphs (SKGs) are vital for research discovery and impact analysis.
Accurate author name disambiguation (AND) is essential for coherent researcher profiles within SKGs.
Existing AND methods struggle with challenges like homonymy and data sparsity in large-scale SKGs.

Purpose of the Study:

To specifically address author name disambiguation (AND) methods within the context of scholarly knowledge graphs (SKGs).
To explore how the unique entity-relationship structure of SKGs can be leveraged for improved AND.
To provide a comprehensive overview and evaluation framework for graph-based AND techniques in SKGs.

Main Methods:

Review and discussion of recent graph-based methods for author name disambiguation (AND).
Definition of a standardized framework for comparing and evaluating different AND approaches.
Cataloging of popular datasets and benchmarks used for testing AND methods in SKGs.

Main Results:

Identified limitations of traditional AND methods in the context of SKGs.
Highlighted the potential of graph-based approaches by exploiting SKG structures.
Established a basis for systematic comparison and benchmarking of AND methods.

Conclusions:

Graph-based methods offer promising solutions for author name disambiguation (AND) in scholarly knowledge graphs (SKGs).
A unified framework is needed for robust evaluation and advancement of AND techniques.
Future research should focus on leveraging SKG structures for more accurate and scalable AND.