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Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives

Wenjin XieSiyuan LiuXiaomeng WangTao Jia
Dec 2022
Name ambiguity is common in academic digital libraries, such as multipleauthors having the same name. This creates challenges for academic datamanagement and analysis, thus name disambiguation becomes necessary. Theprocedure of name disambiguation is to divide publications with the same nameinto different groups, each group belonging to a unique author. A large amountof attribute information in publications makes traditional methods fall intothe quagmire of feature selection. These methods always select attributesartificially and equally, which usually causes a negative impact on accuracy.The proposed method is mainly based on representation learning forheterogeneous networks and clustering and exploits the self-attentiontechnology to solve the problem. The presentation of publications is asynthesis of structural and semantic representations. The structuralrepresentation is obtained by meta-path-based sampling and a skip-gram-basedembedding method, and meta-path level attention is introduced to automaticallylearn the weight of each feature. The semantic representation is generatedusing NLP tools. Our proposal performs better in terms of name disambiguationaccuracy compared with baselines and the ablation experiments demonstrate theimprovement by feature selection and the meta-path level attention in ourmethod. The experimental results show the superiority of our new method forcapturing the most attributes from publications and reducing the impact ofredundant information.