Name Disambiguation Based on Similar Features and Relation Graph Optimization
Cui Huanqing1,2(),Yang Junzhu1,Song Weiqing1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China 2State Key Laboratory of High-end Server & Storage Technology, Inspur Group Co., Ltd., Jinan 250014, China
[Objective] The paper aims to fully utilize the feature information and relation information of academic literature to improve author name disambiguation. [Methods] We proposed a name disambiguation method combining feature information embedding and relation graph optimization. First, we extracted feature information from literature and applied representation learning to obtain the embedding vectors. Then, we mined the relationship information between literatures, and also constructed four relation graphs to optimize the embedding vectors of each literature. Finally, we used hierarchical agglomerative clustering algorithm to obtain the disambiguation results. [Results] We examined the new model on AMiner-na dataset and found its average F1 score reached 68.78%, which was 1.81 percent points higher than the second best method. [Limitations] The proposed method focuses on the average disambiguation effect of all authors, and the disambiguation effect of some authors needs to be improved. [Conclusions] The proposed method can fully utilize the literature relation information, and effectively improve the effect of author name disambiguation.
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Cui Huanqing, Yang Junzhu, Song Weiqing. Name Disambiguation Based on Similar Features and Relation Graph Optimization. Data Analysis and Knowledge Discovery, 2023, 7(5): 71-80.
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