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On computing similarity in academic literature data: Methods and evaluation

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dc.contributor.authorHamedani, Masoud Reyhani-
dc.contributor.authorKim, Sang-Wook-
dc.date.accessioned2022-07-16T02:37:42Z-
dc.date.available2022-07-16T02:37:42Z-
dc.date.created2021-05-13-
dc.date.issued2014-10-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158936-
dc.description.abstractSimilarity 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.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleOn computing similarity in academic literature data: Methods and evaluation-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Sang-Wook-
dc.identifier.doi10.1007/978-3-319-11538-2_37-
dc.identifier.scopusid2-s2.0-84910068038-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.8597, pp.403 - 412-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume8597-
dc.citation.startPage403-
dc.citation.endPage412-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusAcademic literature-
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