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

Marie KATSURAI  Ikki OHMUKAI  Hideaki TAKEDA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.4   pp.1010-1018
Publication Date: 2016/04/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)
Category: 
Keyword: 
researcher analysis,  academic database,  topic model,  author disambiguation,  

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Summary: 
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.