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dc.creatorKovačević, Ana
dc.date.accessioned2020-01-08T16:45:49Z
dc.date.available2020-01-08T16:45:49Z
dc.date.issued2009
dc.identifier.isbn978-1-4419-1218-3
dc.identifier.urihttps://rhinosec.fb.bg.ac.rs/handle/123456789/58
dc.description.abstractThe paper proposes matching short forms (abbreviated titles from the citation report) with their corresponding longer ones (journal titles in the digital library). The main problem is that there are often a number of syntactically different abbreviated forms for one abbreviated title in the citation report. We use character- and token-based similarity metrics to identify duplicate records. Also, we improve the process of identifying syntactically different data with the automated discovery of ontological knowledge representations such as thesauri from correctly matched data.en
dc.publisherBoston : Springer
dc.rightsrestrictedAccess
dc.sourceWeb 2.0 & Semantic Web
dc.subjectontologies
dc.subjectdata mining
dc.subjectdigital libraries
dc.titleOntology-based data mining in digital librariesen
dc.typeconferenceObject
dc.rights.licenseARR
dcterms.abstractКовачевић, Aна;
dc.citation.spage163
dc.citation.epage175
dc.citation.other: 163-175
dc.description.otherAnnals of Information Systems, vol. 6
dc.identifier.doi10.1007/978-1-4419-1219-0_7
dc.type.versionpublishedVersion


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