Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives

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dc.identifier.uri http://dx.doi.org/10.15488/16289
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/16416
dc.contributor.author Souza, Tarcisio
dc.contributor.author Demidova, Elena
dc.contributor.author Risse, Thomas
dc.contributor.author Holzmann, Helge
dc.contributor.author Gossen, Gerhard
dc.contributor.author Szymanski, Julian
dc.contributor.editor Cardoso, Jorge
dc.contributor.editor Guerra, Francesco
dc.contributor.editor Houben, Geert-Jan
dc.contributor.editor Pinto, Alexandre Miguel
dc.contributor.editor Velegrakis, Yannis
dc.date.accessioned 2024-02-13T08:26:16Z
dc.date.available 2024-02-13T08:26:16Z
dc.date.issued 2016
dc.identifier.citation Souza, T.; Demidova, E.; Risse, T.; Holzmann, H.; Gossen, G. et al.: Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives. In: Cardoso, Jorge; Guerra, Francesco; Houben, Geert-Jan; Pinto, Alexandre Miguel; Velegrakis, Yannis (Eds.): Semantic keyword-based search on structured data sources : first COST Action IC1302 International KEYSTONE Conference, IKC 2015. Berlin ; Heidelberg : Springer, 2016 (Lecture Notes in Computer Science (LNCS) ; 9398), S. 153-166. DOI: https://doi.org/10.1007/978-3-319-27932-9_14
dc.description.abstract Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files. The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the Popular German Web dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents. eng
dc.language.iso eng
dc.publisher Berlin ; Heidelberg : Springer
dc.relation.ispartof Semantic keyword-based search on structured data sources : first COST Action IC1302 International KEYSTONE Conference, IKC 2015
dc.relation.ispartofseries Lecture Notes in Computer Science (LNCS) ; 9398
dc.rights CC BY-NC 2.5 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc/2.5/
dc.subject Arches eng
dc.subject Data mining eng
dc.subject Extraction eng
dc.subject Natural language processing systems eng
dc.subject Semantic Web eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 004 | Informatik
dc.title Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives eng
dc.type BookPart
dc.type Text
dc.relation.essn 1611-3349
dc.relation.isbn 978-3-319-27932-9
dc.relation.isbn 978-3-319-27931-2
dc.relation.issn 0302-9743
dc.relation.doi https://doi.org/10.1007/978-3-319-27932-9_14
dc.bibliographicCitation.volume 9398
dc.bibliographicCitation.firstPage 153
dc.bibliographicCitation.lastPage 166
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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