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Unsupervised author disambiguation using Dempster–Shafer theory

Author

Listed:
  • Hao Wu

    (Yunnan University)

  • Bo Li

    (Yunnan University)

  • Yijian Pei

    (Yunnan University)

  • Jun He

    (Nanjing University of Information Science and Technology)

Abstract

The name ambiguity problem presents many challenges for scholar finding, citation analysis and other related research fields. To attack this issue, various disambiguation methods combined with separate disambiguation features have been put forward. In this paper, we offer an unsupervised Dempster–Shafer theory (DST) based hierarchical agglomerative clustering algorithm for author disambiguation tasks. Distinct from existing methods, we exploit the DST in combination with Shannon’s entropy to fuse various disambiguation features and come up with a more reliable candidate pair of clusters for amalgamation in each iteration of clustering. Also, some solutions to determine the convergence condition of the clustering process are proposed. Depending on experiments, our method outperforms three unsupervised models, and achieves comparable performances to a supervised model, while does not prescribe any hand-labelled training data.

Suggested Citation

  • Hao Wu & Bo Li & Yijian Pei & Jun He, 2014. "Unsupervised author disambiguation using Dempster–Shafer theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1955-1972, December.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:3:d:10.1007_s11192-014-1283-x
    DOI: 10.1007/s11192-014-1283-x
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    References listed on IDEAS

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    6. KM. Pooja & Samrat Mondal & Joydeep Chandra, 2021. "Exploiting similarities across multiple dimensions for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7525-7560, September.

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