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dc.identifier.uri http://dx.doi.org/10.15488/10888
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/10970
dc.contributor.author Stubbemann, Maximilian
dc.contributor.author Hanika, Tom
dc.contributor.author Stumme, Gerd
dc.contributor.editor Berthold, Michael R.
dc.contributor.editor Feelders, Ad
dc.contributor.editor Krempl, Georg
dc.date.accessioned 2021-05-06T09:13:18Z
dc.date.available 2021-05-06T09:13:18Z
dc.date.issued 2020
dc.identifier.citation Stubbemann, M.; Hanika, T.; Stumme, G.: Orometric methods in bounded metric data. In: Berthold, M.R.; Feelders, A.; Krempl, G. (Eds.): Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. Cham : Springer International Publishing, 2020 (Lecture notes in computer science ; 12080), S. 496-508. DOI: https://doi.org/10.1007/978-3-030-44584-3_39
dc.description.abstract A large amount of data accommodated in knowledge graphs (KG) is metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like cities or celestial objects. In this paper, we propose a novel approach that transfers orometric (topographic) measures to bounded metric spaces. While these methods were originally designed to identify relevant mountain peaks on the surface of the earth, we demonstrate a notion to use them for metric data sets in general. Notably, metric sets of items enclosed in knowledge graphs. Based on this we present a method for identifying outstanding items using the transferred valuations functions isolation and prominence. Building up on this we imagine an item recommendation process. To demonstrate the relevance of the valuations for such processes, we evaluate the usefulness of isolation and prominence empirically in a machine learning setting. In particular, we find structurally relevant items in the geographic population distributions of Germany and France. © 2020, The Author(s). eng
dc.language.iso eng
dc.publisher Berlin ; Heidelberg : Springer
dc.relation.ispartof Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings
dc.relation.ispartofseries Lecture notes in computer science ; 12080
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject information analysis eng
dc.subject celestial objects eng
dc.subject knowledge graphs eng
dc.subject large amounts eng
dc.subject metric spaces eng
dc.subject mountain peaks eng
dc.subject data handling eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 004 | Informatik ger
dc.title Orometric methods in bounded metric data
dc.type BookPart
dc.type Text
dc.relation.essn 1611-3349
dc.relation.isbn 978-3-030-44583-6
dc.relation.isbn 978-3-030-44584-3
dc.relation.issn 0302-9743
dc.relation.doi https://doi.org/10.1007/978-3-030-44584-3_39
dc.bibliographicCitation.volume 12080
dc.bibliographicCitation.firstPage 496
dc.bibliographicCitation.lastPage 508
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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