Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312268 
Year of Publication: 
2023
Citation: 
[Journal:] International Journal on Digital Libraries [ISSN:] 1432-1300 [Volume:] 25 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 431-441
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.
Subjects: 
Author name disambiguation
Entity linkage
Bibliographic data
Neural networks
Classification
DBLP
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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