[Objective] The paper tries to improve author name disambiguation with entity relationship data from academic literature. [Methods] First, we extracted multi-type nodes and their relationships from literature to construct a heterogeneous information network (HIN). Then, we applied representation learning to obtain the latent vectors of authors, and used clutering analysis to get a preliminary division. Finally, we merged several clusters based on strong rule matching to obtain the disambiguation. [Results] We examined the new model with dataset from the Web of Science. The K-Metric mean value was 0.842, a 63.18% increase over the baseline model. Without strong rule matching, the improvement also reached 34.69%. [Limitations] The proposed model requires citation information, which limited its application scenarios. [Conclusions] Our new method could effectively improve the performance of author name disambiguation.
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Deng Qiping, Chen Weijing, Ji Ling, Zhang Yu’e. Author Name Disambiguation Based on Heterogeneous Information Network. Data Analysis and Knowledge Discovery, 2022, 6(4): 60-68.
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