Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/312268 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] International Journal on Digital Libraries [ISSN:] 1432-1300 [Volume:] 25 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 431-441
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Author name disambiguation
Entity linkage
Bibliographic data
Neural networks
Classification
DBLP
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