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| Method for Author Name Disambiguation in Specific Research Tasks |
| Wu Keye1,2, Min Chao1,2, Sun Jianjun1,2, Quan Zhaoxuan1,2 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Institute of Data Research in Humanities and Social Sciences, Nanjing University, Nanjing 210023 |
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Abstract Author name disambiguation is usually required in analyzing the flow of talents and evaluating scholars in academic works. This paper proposes an accurate and convenient method for name disambiguation for a specific research task. In order to simplify calculations and account for the lack of local data, this paper constructs a two-stage name disambiguation framework based on heterogeneous data. The first stage involves fully mining the local associated data, and the second stage combines the authoritative external data. Based on representation, relevant information extraction, relational network construction, semi-fuzzy retrieval, and other steps are carried out to achieve comprehensive and objective name disambiguation. Finally, the superiority of this method is identified and verified through thesis data under the field of artificial intelligence. Compared with manually annotated data, the framework performs better in disambiguation, and solves the problem of synonyms and namesakes in the original data, thus laying a solid foundation for subsequent research tasks.
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Received: 01 June 2020
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