A Collective Approach to Scholar Name Disambiguation

Dongsheng Luo, Shuai Ma, Yaowei Yan, Chunming Hu, Xiang Zhang, Jinpeng Huai

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Scholar name disambiguation remains a hard and unsolved problem, which brings various troubles for bibliography data analytics. Most existing methods handle name disambiguation separately that tackles one name at a time, and neglect the fact that disambiguation of one name affects the others. Further, it is typically common that only limited information is available for bibliography data, e.g., only basic paper and citation information is available in DBLP. In this study, we propose a collective approach to name disambiguation, which takes the connection of different ambiguous names into consideration. We reformulate bibliography data as a heterogeneous multipartite network, which initially treats each author reference as a unique author entity, and disambiguation results of one name propagate to the others of the network. To further deal with the sparsity problem caused by limited available information, we also introduce word-word and venue-venue similarities, and we finally measure author similarities by assembling similarities from four perspectives. Using real-life data, we experimentally demonstrate that our approach is both effective and efficient.

Original languageEnglish (US)
Pages (from-to)2020-2032
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number5
DOIs
StatePublished - May 1 2022

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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