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Open AccessArticle

A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

by Yingying Ma 1,2,3, Youlong Wu 1,* and Chengqiang Lu 4
1
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
2
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
University of Science and Technology of China, Heifei 230026, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 416; https://doi.org/10.3390/e22040416
Received: 6 March 2020 / Revised: 31 March 2020 / Accepted: 4 April 2020 / Published: 7 April 2020
(This article belongs to the Special Issue Deep Artificial Neural Networks Meet Information Theory)
Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysis performance. To solve this problem, an author name disambiguation task is designed to divide documents related to an author name reference into several parts and each part is associated with a real-life person. Existing methods usually use either attributes of documents or relationships between documents and co-authors. However, methods of feature extraction using attributes cause inflexibility of models while solutions based on relationship graph network ignore the information contained in the features. In this paper, we propose a novel name disambiguation model based on representation learning which incorporates attributes and relationships. Experiments on a public real dataset demonstrate the effectiveness of our model and experimental results demonstrate that our solution is superior to several state-of-the-art graph-based methods. We also increase the interpretability of our method through information theory and show that the analysis could be helpful for model selection and training progress. View Full-Text
Keywords: name disambiguation; graph neural network; clustering analysis; mutual information name disambiguation; graph neural network; clustering analysis; mutual information
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MDPI and ACS Style

Ma, Y.; Wu, Y.; Lu, C. A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory. Entropy 2020, 22, 416.

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