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Unsupervised author disambiguation using Dempster–Shafer theory

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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.

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Notes

  1. http://www.informatik.uni-trier.de/ley/db/

  2. http://www.arnetminer.org/disambiguation

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Acknowledgments

This work is supported by the Scientific Research Project of Yunnan University (2010YB024) and the Applied Basic Research Project of Yunnan Province (2013FB009). This work of Jun He is supported by the National Natural Science Foundation of China (61203273). We are grateful to anonymous reviewers for their useful comments and suggestions which contribute to substantially improving this paper.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Hao Wu, Bo Li & Yijian Pei

  2. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jun He

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  1. Hao Wu
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  2. Bo Li
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  3. Yijian Pei
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  4. Jun He
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Correspondence to Hao Wu.

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Wu, H., Li, B., Pei, Y. et al. Unsupervised author disambiguation using Dempster–Shafer theory. Scientometrics 101, 1955–1972 (2014). https://doi.org/10.1007/s11192-014-1283-x

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