Name Disambiguation Based on Heterogeneous Network Representation Learning
TANG Zhengzheng1,2, HONG Xuehai2,3, WANG Yang1,2, LI Yuxuan1,2
1. Center of Information Development Strategy and Evaluation, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190 2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049 3. Strategy Research Center of Information Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190
Abstract：During the search for the name of an author in the system, the return of all documents of the author deteriorates the user experience. Name disambiguation can improve the retrieval accuracy. Therefore, a name disambiguation method based on heterogeneous network representation learning is proposed. Firstly, a paper heterogeneous network is constructed for each ambiguous name. Then, the representation vector of each paper node in the network is obtained based on the heterogeneous network and the Word2Vec. Finally, papers are divided up and assigned to different author entities via rule matching and a clustering method based on density with noise. The proposed method generates better performance on OAG-WholsWho competition dataset, and its effectiveness is verified.
唐正正, 洪学海, 汪洋, 李玉轩. 基于异质网络表示学习的姓名消歧[J]. 模式识别与人工智能, 2021, 34(6): 485-496.
TANG Zhengzheng, HONG Xuehai, WANG Yang, LI Yuxuan. Name Disambiguation Based on Heterogeneous Network Representation Learning. , 2021, 34(6): 485-496.
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