Topic Representation of Researchers' Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation

• KATSURAI Marie

  Doshisha University

• OHMUKAI Ikki

  National Institute of Informatics

• TAKEDA Hideaki

  National Institute of Informatics

Abstract

It is crucial to promote interdisciplinary research and recommend collaborators from different research fields via academic database analysis. This paper addresses a problem to characterize researchers' interests with a set of diverse research topics found in a large-scale academic database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert the textual features of a researcher's publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher's interest as a single vector. In experiments conducted on CiNii Articles, which is the largest academic database in Japan, we show that the extracted topics reflect the diversity of the research fields in the database. The experiment results also indicate the applicability of the proposed topic representation to the author disambiguation problem.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E99.D (4), 1010-1018, 2016

  The Institute of Electronics, Information and Communication Engineers

Keywords

• researcher analysis
• academic database
• topic model
• author disambiguation

Details

• CRID: 1390282679354273280
• NII Article ID: 130005141367
• DOI: 10.1587/transinf.2015dap0031
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E99.D/4/E99.D_2015DAP0031/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed