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| Author Name Disambiguation Using BP Neural Networks under Missing Data |
| Ke Hao1, Li Tian1, Zhou Yue1, Zhong Yuying1, Yu Zhenglu2, Yuan Junpeng3, 4 |
1. Central University of Finance and Economics, Beijing 100081;
2. Institute of Scientific and Technical Information of China, Beijing 100038;
3. National Science Library, Chinese Academy of Sciences, Beijing 100190;
4. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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Abstract Author name disambiguation plays an important role in individual-based information science, knowledge management, bibliometrics, and scientometrics. Machine learning algorithms such as clustering and classification are often employed for author name disambiguation. However, inadequate information and missing field data in metadata of publications can lead to algorithm failure. To address the said issue, this study focuses on the accurate determination of the effect of various fields in author name disambiguation under missing data. Furthermore, the proposed study analyzes existing information for determining the contributory role of each field in author name disambiguation. A unique combination of indicators was designed, and an algorithm for identifying author name repetition by incorporating BP (back propagation) neural networks was developed. The validity and reliability of the proposed algorithm was verified by employing the paper written by “Wang Wei”.
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Received: 04 December 2017
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