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Graph-based methods for Author Name Disambiguation: a survey

Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a...

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Detalles Bibliográficos
Autores principales: De Bonis, Michele, Falchi, Fabrizio, Manghi, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557506/
https://www.ncbi.nlm.nih.gov/pubmed/37810360
http://dx.doi.org/10.7717/peerj-cs.1536
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author De Bonis, Michele
Falchi, Fabrizio
Manghi, Paolo
author_facet De Bonis, Michele
Falchi, Fabrizio
Manghi, Paolo
author_sort De Bonis, Michele
collection PubMed
description Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers’ activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.
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spelling pubmed-105575062023-10-07 Graph-based methods for Author Name Disambiguation: a survey De Bonis, Michele Falchi, Fabrizio Manghi, Paolo PeerJ Comput Sci Artificial Intelligence Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers’ activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic. PeerJ Inc. 2023-09-11 /pmc/articles/PMC10557506/ /pubmed/37810360 http://dx.doi.org/10.7717/peerj-cs.1536 Text en ©2023 De Bonis et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
De Bonis, Michele
Falchi, Fabrizio
Manghi, Paolo
Graph-based methods for Author Name Disambiguation: a survey
title Graph-based methods for Author Name Disambiguation: a survey
title_full Graph-based methods for Author Name Disambiguation: a survey
title_fullStr Graph-based methods for Author Name Disambiguation: a survey
title_full_unstemmed Graph-based methods for Author Name Disambiguation: a survey
title_short Graph-based methods for Author Name Disambiguation: a survey
title_sort graph-based methods for author name disambiguation: a survey
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557506/
https://www.ncbi.nlm.nih.gov/pubmed/37810360
http://dx.doi.org/10.7717/peerj-cs.1536
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