On computing similarity in academic literature data: Methods and evaluation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationWeb-Age Information Management - WAIM 2014 International Workshops
Subtitle of host publicationBigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers
EditorsWolf-Tilo Balke, Jianliang Xu, Peiquan Jin, Tiffany Tang, Xin Lin, Eenjun Hwang, Yueguo Chen, Wei Xu
PublisherSpringer Verlag
Pages403-412
Number of pages10
ISBN (Electronic)9783319115375
DOIs
StatePublished - 2014 Jan 1
Event36th German Conference on Pattern Recognition, GCPR 2014 - Münster, Germany
Duration: 2014 Sep 22014 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8597
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other36th German Conference on Pattern Recognition, GCPR 2014
CountryGermany
CityMünster
Period14/09/214/09/5

Fingerprint

Information retrieval
Data mining
Computing
Evaluation
Experiments
Information Retrieval
Data Mining
Evaluate
Experiment
Similarity

Cite this

Hamedani, R., & Kim, S-W. (2014). On computing similarity in academic literature data: Methods and evaluation. In W-T. Balke, J. Xu, P. Jin, T. Tang, X. Lin, E. Hwang, Y. Chen, ... W. Xu (Eds.), Web-Age Information Management - WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers (pp. 403-412). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8597). Springer Verlag. https://doi.org/10.1007/978-3-319-11538-2_37
Hamedani, Reyhani ; Kim, Sang-Wook. / On computing similarity in academic literature data : Methods and evaluation. Web-Age Information Management - WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers. editor / Wolf-Tilo Balke ; Jianliang Xu ; Peiquan Jin ; Tiffany Tang ; Xin Lin ; Eenjun Hwang ; Yueguo Chen ; Wei Xu. Springer Verlag, 2014. pp. 403-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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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Hamedani, R & Kim, S-W 2014, On computing similarity in academic literature data: Methods and evaluation. in W-T Balke, J Xu, P Jin, T Tang, X Lin, E Hwang, Y Chen & W Xu (eds), Web-Age Information Management - WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8597, Springer Verlag, pp. 403-412, 36th German Conference on Pattern Recognition, GCPR 2014, Münster, Germany, 14/09/2. https://doi.org/10.1007/978-3-319-11538-2_37

On computing similarity in academic literature data : Methods and evaluation. / Hamedani, Reyhani; Kim, Sang-Wook.

Web-Age Information Management - WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers. ed. / Wolf-Tilo Balke; Jianliang Xu; Peiquan Jin; Tiffany Tang; Xin Lin; Eenjun Hwang; Yueguo Chen; Wei Xu. Springer Verlag, 2014. p. 403-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8597).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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N2 - 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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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Hamedani R, Kim S-W. On computing similarity in academic literature data: Methods and evaluation. In Balke W-T, Xu J, Jin P, Tang T, Lin X, Hwang E, Chen Y, Xu W, editors, Web-Age Information Management - WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Revised Selected Papers. Springer Verlag. 2014. p. 403-412. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-11538-2_37