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2018 , Vol. 62 >Issue 3: 55 - 63

DOI: https://doi.org/10.13266/j.issn.0252-3116.2018.03.007

Multi-expertise Researcher Identification: A Case Study of the Big Data

  • Liu Xiaoyu ,
  • Zhu Donghua ,
  • Wang Xuefeng ,
  • Huang Ying
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  • School of Management and Economics, Beijing Institute of Technology, Beijing 100081

Received date: 2017-08-26

  Revised date: 2017-11-20

  Online published: 2018-02-05

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Abstract

[Purpose/significance]In response to the rapid shifting of knowledge needs, how to choose the appropriate researchers for a given problem is an important issue for the government, companies, as well as research institutions. When we face a real complex problem, it is essential to find multi-expertise researchers. This research aims to find a proper way to identify multi-expertise researchers. [Method/process]This paper used a Term Frequency-Inverse Document Frequency (TFIDF) weighted overlapping K-means clustering method. Based on the researchers' co-authorship network built up from the publication data, the TFIDF weighted overlapping K-means clustering method was applied to cluster researchers into overlapping clusters and identify the multi-expertise researchers. [Result/conclusion]Results show that the TFIDF weighted overlapping K-means method has an advantage over the previous work in terms of the precision ratio, the recall ratio and the F-value, so such a method can be beneficial to identify multi-expertise researchers.

Key words： researcher identification; overlapping K-means; multi-expertise researcher; big data; Term Frequency-Inverse Document Frequency (TFIDF)

Cite this article

Liu Xiaoyu , Zhu Donghua , Wang Xuefeng , Huang Ying . Multi-expertise Researcher Identification: A Case Study of the Big Data[J]. Library and Information Service, 2018 , 62(3) : 55 -63 . DOI: 10.13266/j.issn.0252-3116.2018.03.007

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