Multiple features driven Author Name Disambiguation

Qian Zhou, Wei Chen, Weiqing Wang, Jiajie Xu, Lei Zhao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


Author Name Disambiguation (AND) has received more attention recently, accompanied by the increase of academic publications. To tackle the AND problem, existing studies have proposed many approaches based on different types of information, such as raw document feature (e.g., co-author, title, and keywords), fusion feature (e.g., a hybrid publication embedding based on raw document feature), local structural information (e.g., a publication's neighborhood information on a graph), and global structural information (e.g., the interactive information between a node and others on a graph). However, there has been no work taking all the above-mentioned information into account for the AND problem so far. To fill the gap, we propose a novel framework namely MFAND (Multiple Features Driven Author Name Disambiguation). Specifically, we first employ the raw document and fusion feature to construct six similarity graphs for each author name to be disambiguated. Next, the global and local structural information extracted from these graphs is fed into a novel encoder called R3JG, which integrates and reconstructs the above-mentioned four types of information associated with an author, with the goal of learning the latent information to enhance the generalization ability of the MFAND. Then, the integrated and reconstructed information is fed into a binary classification model for disambiguation. Note that, several pruning strategies are applied before the information extraction to remove noise effectively. Finally, our proposed framework is investigated on two real-world datasets, and the experimental results show that MFAND performs better than all state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Web Services, ICWS 2021
EditorsCarl K. Chang, Ernesto Damiani, Jing Fan, Parisa Ghodous, Michael Maximilien, Zhongjie Wang, Robert Ward, Jia Zhang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781665416818
ISBN (Print)9781665416825
Publication statusPublished - 2021
EventIEEE International Conference on Web Services 2021 - Online, United States of America
Duration: 5 Sept 202111 Sept 2021 (Proceedings)


ConferenceIEEE International Conference on Web Services 2021
Abbreviated titleICWS 2021
Country/TerritoryUnited States of America
Internet address


  • author name disambiguation
  • binary classification
  • multiple features
  • pruning strategy

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