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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-10557506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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