Author Name Disambiguation Based on Semi-supervised Learning with Graph Convolutional Network

Xiaoguang SHENG; Ying WANG; Li QIAN; Ying WANG

doi:10.11999/JEIT200905

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3442-3450

Xiaoguang SHENG, Ying WANG, Li QIAN, Ying WANG. Author Name Disambiguation Based on Semi-supervised Learning with Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3442-3450. doi: 10.11999/JEIT200905

Citation:

Xiaoguang SHENG, Ying WANG, Li QIAN, Ying WANG. Author Name Disambiguation Based on Semi-supervised Learning with Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3442-3450. doi: 10.11999/JEIT200905

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Author Name Disambiguation Based on Semi-supervised Learning with Graph Convolutional Network

doi: 10.11999/JEIT200905

Xiaoguang SHENG^{1
,
,},
Ying WANG²,
Li QIAN^{2, 3},
Ying WANG¹

1.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
2.
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
3.
Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (61702038), The National Social Science Foundation of China (15CTQ006)

Received Date: 2020-10-23
Accepted Date: 2021-11-04
Rev Recd Date: 2021-09-23

Available Online: 2021-11-10

Publish Date: 2021-12-21

Abstract

Abstract

In order to solve the problem of exact matching between scholars and articles, a new method of author name disambiguation is proposed based on semi-supervised learning with graph convolutional network. In this method, the SciBERT pre-training language model is applied to calculating the semantic embedding vector of each paper with their title and keywords. Authors and organizations of papers are used to obtain the adjacency matrixes of the paper’s co-author network and co-organization network. The pseudo labels are collected from the co-author network to obtain the positive and negative samples. The semantic embedding vector, adjacency matrixes and the positive and negative samples are used as input to be processed by Graph Convolution neural Network (GCN). In semi-supervised learning, the embedding vectors of papers are learned to be clustered in order to realize the name disambiguation of papers. The experimental results show that, compared with other disambiguation methods, this method achieves better results on the experimental dataset.
- Name disambiguation,
- Graph Convolutional Network (GCN),
- BERT language model

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References(24)

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