Please use this identifier to cite or link to this item: https://oa.tib.eu/renate/handle/123456789/6098
Files in This Item:
File Description SizeFormat 
Tasnim2020_Chapter_Chapter8Context-BasedEntityMat.pdf3,66 MBAdobe PDFView/Open
Title: Context-Based Entity Matching for Big Data
Authors: Tasnim, MayeshaCollarana, DiegoGraux, DamienVidal, Maria-Esther
Editors: Janev, ValentinaGraux, DamienJabeen, HajiraSallinger, Emanuel
Publishers version: https://doi.org/10.1007/978-3-030-53199-7_8
URI: https://oa.tib.eu/renate/handle/123456789/6098
https://doi.org/10.34657/5080
Issue Date: 2020
Published in: Lecture Notes in Computer Science
Book: Knowledge Graphs and Big Data Processing
Journal: Lecture Notes in Computer Science
Volume: 12072
Page Start: 122
Page End: 146
Publisher: Cham : Springer
Abstract: In 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.
Keywords: LAMBDA Project; RDF; Big Data
Type: bookPart; Text
Publishing status: publishedVersion
DDC: 004
License: CC BY 4.0 Unported
Link to license: https://creativecommons.org/licenses/by/4.0/
Appears in Collections:Informationswissenschaften

Show full 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