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Towards Effective Author Name Disambiguation by Hybrid Attention

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Abstract

Author name disambiguation (AND) is a central task in academic search, which has received more attention recently accompanied by the increase of authors and academic publications. To tackle the AND problem, existing studies have proposed various approaches based on different types of information, such as raw document features (e.g., co-authors, titles, and keywords), the fusion feature (e.g., a hybrid publication embedding based on multiple raw document features), the local structural information (e.g., a publication’s neighborhood information on a graph), and the global structural information (e.g., interactive information between a node and others on a graph). However, there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so far. To fill the gap, we propose a novel framework named EAND (Towards Effective Author Name Disambiguation by Hybrid Attention). Specifically, we design a novel feature extraction model, which consists of three hybrid attention mechanism layers, to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients, raw document features, and the fusion feature. Each hybrid attention mechanism layer contains three key modules: a local structural perception, a global structural perception, and a feature extractor. Additionally, the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector space. Experimental results on two real-world datasets demonstrate that EAND achieves superior performance, outperforming state-of-the-art methods by at least +2.74% in terms of the micro-F1 score and +3.31% in terms of the macro-F1 score.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Qian Zhou  (周 乾), Wei Chen  (陈 伟), Peng-Peng Zhao  (赵朋朋), An Liu  (刘 安), Jia-Jie Xu  (许佳捷), Jian-Feng Qu  (瞿剑峰) & Lei Zhao  (赵 雷)

Authors
  1. Qian Zhou  (周 乾)
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  2. Wei Chen  (陈 伟)
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  3. Peng-Peng Zhao  (赵朋朋)
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  4. An Liu  (刘 安)
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  5. Jia-Jie Xu  (许佳捷)
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  6. Jian-Feng Qu  (瞿剑峰)
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Corresponding authors

Correspondence to Wei Chen  (陈 伟) or Lei Zhao  (赵 雷).

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Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

A preliminary version of the paper was published in the proceedings of ICWS 2021.

This work was supported by the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant Nos. 19KJA610002 and 19KJB520050, and the National Natural Science Foundation of China under Grant No. 61902270.

Chen Wei is the principal investigator of the second and third funding projects; Zhao Lei is the designer of the research framework and also the principal investigator of the first funding project.

Qian Zhou received his M.S. degree in computer science and technology from Soochow University, Suzhou, in 2022. Currently, he is a research assistant at the School of Computer Science and Technology, Soochow University, Suzhou. His current research interests mainly include data mining, deep learning, and natural language processing.

Wei Chen is currently an associate professor in the School of Computer Science and Technology at Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2018. His research interests include heterogeneous information network analysis, cross-platform linkage and recommendation, spatio-temporal database, and knowledge graph embedding and refinement.

Peng-Peng Zhao received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2008. He is a professor at the School of Computer Science and Technology at Soochow University, Suzhou. His current research interests include data mining, deep learning, big data analysis, and recommender systems.

An Liu is a professor at the School of Computer Science and Technology, Soochow University, Suzhou. He received his Ph.D. degree in computer science from both City University of Hong Kong, Hong Kong, and University of Science and Technology of China, Hefei, in 2009. His research interests include security, privacy, trust in emerging applications, cloud computing, and services computing.

Jia-Jie Xu is an associate professor at the School of Computer Science and Technology, Soochow University, Suzhou. He got his Ph.D. and M.S. degrees from the Swinburne University of Technology, Melbourne, and the University of Queensland, Brisbane, in 2011 and 2006, respectively. His research interests mainly include spatio-temporal database systems, big data analytics, and workflow systems.

Jian-Feng Qu is a lecturer at the School of Computer Science and Technology, Soochow University, Suzhou. He received his B.S., M.S., and Ph.D. degrees in computer science from Jilin University, Changchun, in 2013, 2016 and 2019, respectively. His research interests include information extraction, data mining, natural language processing, and deep learning.

Lei Zhao is a professor at the School of Computer Science and Technology, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2006. His recent research is to analyze large graph databases in an effective, efficient, and secure way.

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Zhou, Q., Chen, W., Zhao, PP. et al. Towards Effective Author Name Disambiguation by Hybrid Attention. J. Comput. Sci. Technol. 39, 929–950 (2024). https://doi.org/10.1007/s11390-023-2070-z

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