Clustering Semantic Predicates in the Open Research Knowledge Graph

dc.bibliographicCitation.bookTitleFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries : 24th International Conference on Asian Digital Libraries, ICADL 2022, Hanoi, Vietnam, November 30 – December 2, 2022, Proceedings
dc.bibliographicCitation.date2022
dc.bibliographicCitation.firstPage477
dc.bibliographicCitation.lastPage484
dc.bibliographicCitation.seriesTitleLecture Notes in Computer Science ; 13636eng
dc.bibliographicCitation.volume13636
dc.contributor.authorArab Oghli, Omar
dc.contributor.authorD’Souza, Jennifer
dc.contributor.authorAuer, Sören
dc.date.accessioned2024-05-10T05:24:23Z
dc.date.available2024-05-10T05:24:23Z
dc.date.issued2022
dc.description.abstractWhen semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.eng
dc.description.versionacceptedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/14590
dc.identifier.urihttps://doi.org/10.34657/13621
dc.language.isoeng
dc.publisherHeidelberg : Springer
dc.relation.doihttps://doi.org/10.1007/978-3-031-21756-2_39
dc.relation.essn1611-3349
dc.relation.issn0302-9743
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed on other websites via the internet or passed on to external parties.eng
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht auf anderen Webseiten im Internet bereitgestellt oder an Außenstehende weitergegeben werden.ger
dc.subject.ddc004
dc.subject.gndKonferenzschriftger
dc.subject.otherArtificial intelligenceeng
dc.subject.otherClustering algorithmseng
dc.subject.otherContent-based recommender systemseng
dc.subject.otherOpen research knowledge grapheng
dc.titleClustering Semantic Predicates in the Open Research Knowledge Grapheng
dc.typeBookParteng
dc.typeTexteng
dcterms.event24th International Conference on Asia-Pacific Digital Libraries, ICADL 2022, 30 November 2022-2 December 2022, Hanoi
tib.accessRightsopenAccess
wgl.contributorTIB
wgl.subjectInformatikger
wgl.typeBuchkapitel / Sammelwerksbeitragger
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