Unsupervised author disambiguation using Dempster–Shafer theory

Hao Wu (), Bo Li, Yijian Pei and Jun He
Additional contact information
Hao Wu: Yunnan University
Bo Li: Yunnan University
Yijian Pei: Yunnan University
Jun He: Nanjing University of Information Science and Technology

Scientometrics, 2014, vol. 101, issue 3, No 20, 1955-1972

Abstract: 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.

Keywords: Author disambiguation; Dempster–Shafer theory of evidence; Hierarchical clustering; Unsupervised (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-014-1283-x

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