On Computing Similarity in Academic Literature Data: Methods and Evaluation

Similarity computation for academic literature data is one of the interesting topics that have been discussed recently in information retrieval and data mining. Consequently, a variety of methods has been proposed to compute the similarity of scientific papers. In this paper, we present various similarity methods and evaluate their effectiveness via extensive experiments on a real-world dataset of scientific papers.This work was supported by (1) Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2013 (Grants No. C0006278), (2) the MSIP (Ministry of Science, ICT, and FuturePlanning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-4009) supervised by the NIPA (National IT Industry Promotion Agency), and (3) the Seoul Creative Human Development Program (HM120006)