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SCIENCE CHINA Information Sciences, Volume 64 , Issue 4 : 144101(2021) https://doi.org/10.1007/s11432-019-9884-y

Name disambiguation in AMiner

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  • ReceivedMar 16, 2019
  • AcceptedApr 23, 2019
  • PublishedJun 18, 2020

Abstract

There is no abstract available for this article.


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Videos and other supplemental documents.


References

[1] Tang J, Zhang J, Yao L M, et al. ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, 2008. 990--998. Google Scholar

[2] Tang J, Fong A C M, Wang B. A Unified Probabilistic Framework for Name Disambiguation in Digital Library. IEEE Trans Knowl Data Eng, 2012, 24: 975-987 CrossRef Google Scholar

[3] Zhang Y, Zhang F, Yao P, et al. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018. 1002--1011. Google Scholar

[4] Hu B, Lu Z, Li H, et al. Convolutional neural network architectures for matching natural language sentences. In: Proceedings of the 27th Advances in neural information processing systems, 2014. 2042--2050. Google Scholar

[5] Leman A, Mary M, Christos F, et al. OddBall: spotting anomalies in weighted graphs. In: Proceedings of the 2010 Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2010. 410--421. Google Scholar

  • Figure 1

    (Color online) (a) An example of name disambiguating results for the researchers named “Jing Zhang" in AMiner. Name disambiguation under three scenarios: (b) full ND; (c) continuous ND; (d) error detection.