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On computing similarity in academic literature data: Methods and evaluation

Authors
Hamedani, Masoud ReyhaniKim, Sang-Wook
Issue Date
Oct-2014
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.8597, pp.403 - 412
Indexed
SCOPUS
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
8597
Start Page
403
End Page
412
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158936
DOI
10.1007/978-3-319-11538-2_37
ISSN
0302-9743
Abstract
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.
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COLLEGE OF ENGINEERING (SCHOOL OF COMPUTER SCIENCE)
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