Please use this identifier to cite or link to this item: https://oa.tib.eu/renate/handle/123456789/6098
Full metadata record
DC FieldValueLanguage
dc.rights.licenseCC BY 4.0 Unportedeng
dc.contributor.authorTasnim, Mayesha-
dc.contributor.authorCollarana, Diego-
dc.contributor.authorGraux, Damien-
dc.contributor.authorVidal, Maria-Esther-
dc.contributor.editorJanev, Valentina-
dc.contributor.editorGraux, Damien-
dc.contributor.editorJabeen, Hajira-
dc.contributor.editorSallinger, Emanuel-
dc.date.accessioned2021-03-18T15:46:51Z-
dc.date.available2021-03-18T15:46:51Z-
dc.date.issued2020-
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6098-
dc.identifier.urihttps://doi.org/10.34657/5080-
dc.description.abstractIn the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets—the match might not be valid in every context. To perform a contextually relevant entity matching, the specific context under which a data-driven task, e.g., data integration is performed, must be taken into account. However, existing approaches only consider inter-schema and properties mapping of different data sources and prevent users from selecting contexts and conditions during a data integration process. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner. In the first step, COMET computes the similarity measures across RDF entities and resorts to the Formal Concept Analysis algorithm to map contextually equivalent RDF entities. Finally, COMET combines the results of the first step and executes a 1-1 perfect matching algorithm for matching RDF entities based on the combined scores. We empirically evaluate the performance of COMET on testbed from DBpedia. The experimental results suggest that COMET accurately matches equivalent RDF graphs in a context-dependent manner.eng
dc.language.isoengeng
dc.publisherCham : Springereng
dc.relation.ispartofKnowledge Graphs and Big Data Processingeng
dc.relation.ispartofseriesLecture Notes in Computer Scienceeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subjectLAMBDA Projecteng
dc.subjectRDFeng
dc.subjectBig Dataeng
dc.subject.ddc004eng
dc.titleContext-Based Entity Matching for Big Dataeng
dc.typebookParteng
dc.typeTexteng
dc.description.versionpublishedVersioneng
wgl.contributorTIBeng
wgl.subjectInformatikeng
wgl.typeBuchkapitel / Sammelwerksbeitrageng
dc.bibliographicCitation.firstPage122eng
dc.bibliographicCitation.lastPage146eng
dc.bibliographicCitation.volume12072eng
dc.relation.doihttps://doi.org/10.1007/978-3-030-53199-7_8-
dc.relation.essn1611-3349-
dc.relation.issn0302-9743-
dc.relation.isbn978-3-030-53198-0-
dcterms.bibliographicCitation.journalTitleLecture Notes in Computer Scienceeng
tib.accessRightsopenAccesseng
Appears in Collections:Informationswissenschaften

Files in This Item:
File Description SizeFormat 
Tasnim2020_Chapter_Chapter8Context-BasedEntityMat.pdf3,66 MBAdobe PDFView/Open
Show simple item record
Tasnim, Mayesha, Diego Collarana, Damien Graux and Maria-Esther Vidal, 2020. Context-Based Entity Matching for Big Data. In: (Hrsg.)Valentina Janev, Damien Graux, Hajira Jabeen and Emanuel Sallinger. Cham : Springer. ISBN 978-3-030-53198-0
Tasnim, M., Collarana, D., Graux, D. and Vidal, M.-E. (2020) “Context-Based Entity Matching for Big Data.” Cham : Springer. doi: https://doi.org/10.1007/978-3-030-53199-7_8.
Tasnim M, Collarana D, Graux D, Vidal M-E. Context-Based Entity Matching for Big Data. In: , editorJanev V, Graux D, Jabeen H, Sallinger E. Cham : Springer; 2020.
Tasnim, M., Collarana, D., Graux, D., & Vidal, M.-E. (2020). Context-Based Entity Matching for Big Data. Cham : Springer. https://doi.org/https://doi.org/10.1007/978-3-030-53199-7_8
Tasnim M, Collarana D, Graux D, Vidal M-E. Context-Based Entity Matching for Big Data. In: , ed.Janev V, Graux D, Jabeen H, Sallinger E Vol. 12072. Cham : Springer; 2020. doi:https://doi.org/10.1007/978-3-030-53199-7_8


This item is licensed under a Creative Commons License Creative Commons