A supervised and distributed framework for cold-start author disambiguation in large-scale publications

Chen, Yibo; Jiang, Zhiyi; Gao, Jianliang; Du, Hongliang; Gao, Liping; Li, Zhao

doi:10.1007/s00521-020-05684-y

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…Depending on the training methodology, a method may be supervised ( i.e. , Rehs (2021) , and Chen et al (2023b) ), unsupervised ( Santini et al (2022) , Qiao et al (2019) , Xiong, Bao & Wu (2021) , Zhang et al (2021) , Chen et al (2021) , Zhou et al (2021) , Zheng et al (2021) , Pooja, Mondal & Chandra (2022) , and Xie et al (2022) ) and semi-supervised ( i.e. , Mihaljević & Santamaría (2021) ).…”

Section: Discussionmentioning

confidence: 99%

“…Pairwise comparison methods ( i.e. , Rehs (2021) , Mihaljević & Santamaría (2021) , and Chen et al (2023b) ) compare nodes of the graph with others to generate similarity vectors in which each element indicates the similarity degree between the target node and the others. Depending on the method, the vector may contain 0 (different author) or 1 (same author), or a similarity degree.…”

Section: Discussionmentioning

confidence: 99%

“…DND. Chen et al (2023b) present a supervised Distributed Framework for Name Disambiguation (so-called DND ), developed as a linkage prediction task to overcome the limitations of knowing the number of clusters a priori. Authors of the framework train a robust function to measure similarities between publications to determine whether they belong to the same author.…”

Section: Methodsmentioning

confidence: 99%

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Graph-based methods for Author Name Disambiguation: a survey

De Bonis,

Falchi,

Manghi

2023

PeerJ Computer Science

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Graph-based methods for Author Name Disambiguation: a survey

De Bonis,

Falchi,

Manghi

2023

PeerJ Computer Science

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“…The diversity of the data is also important, so [10] introduced other databases to enhance the features of the data and achieve better performance. Chen et al [11] used attribute information like institution, coauthors, years as the criteria for the uniqueness of the author, and then adopt the clustering merging strategy to improve the model recall rate. Silva et al [12] proposed a new feature extraction method for the AND task, which can extract the local features associated with the author and the publications.…”

Section: Related Workmentioning

confidence: 99%

Author Name Disambiguation Based on Heterogeneous Graph

Chuang Ma,

Chuang Ma

2023

Journal of Computers

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<p>Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to realize clustering. The task of author name disambiguation is completed to improve the retrieval performance. Experimental results on two public datasets show that our method was improved by 2.8% and 4.9% over the suboptimal method, respectively. Our method can effectively reduce the number of publications returning the wrong author and improve the performance of the academic retrieval system.</p> <p> </p>

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