Instance Matching in Knowledge Graphs ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
Instance Matching in Knowledge Graphs Through Dynamic, Distributed and Affinity-Preserving Random Walk
Auteur(s) :
Assi, Ali [Auteur]
Université du Québec à Montréal = University of Québec in Montréal [UQAM]
Elati, Mohamed [Auteur]
Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Dhifli, Wajdi []
Institut de biologie systémique et synthétique [ISSB]
Université du Québec à Montréal = University of Québec in Montréal [UQAM]
Elati, Mohamed [Auteur]
Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Dhifli, Wajdi []
Institut de biologie systémique et synthétique [ISSB]
Titre de la revue :
IEEE BigData
Pagination :
892-897
Date de publication :
2020-12
Mot(s)-clé(s) en anglais :
random walk
distributed systems
shape matching
distributed systems
shape matching
Discipline(s) HAL :
Informatique [cs]
Résumé en anglais : [en]
Abstract:A key step in the integration of data stored across independent knowledge graphs is to match instances that refer to the same real-world object (e.g., the same person). In this paper, we propose DAP-Walk, a novel ...
Lire la suite >Abstract:A key step in the integration of data stored across independent knowledge graphs is to match instances that refer to the same real-world object (e.g., the same person). In this paper, we propose DAP-Walk, a novel approach for instance matching that is based on a dynamic and affinity-preserving Markov random walk. Our approach takes into account the local and global information mutually calculated from an association graph of candidate pairs of co-referents. Precisely, we leverage this graph to rank each candidate pair through the stationary distribution computed from the random walk on the association graph. We provide a scalable Spark-based implementation for DAP-Walk where the ranking of nodes of candidate co-referents is obtained by aggregating the distributed ranks calculated across spark workers. Experimental results on benchmark datasets show the efficiency and scalability of DAP-Walk compared to several state-of-the-art instance matching approaches.Lire moins >
Lire la suite >Abstract:A key step in the integration of data stored across independent knowledge graphs is to match instances that refer to the same real-world object (e.g., the same person). In this paper, we propose DAP-Walk, a novel approach for instance matching that is based on a dynamic and affinity-preserving Markov random walk. Our approach takes into account the local and global information mutually calculated from an association graph of candidate pairs of co-referents. Precisely, we leverage this graph to rank each candidate pair through the stationary distribution computed from the random walk on the association graph. We provide a scalable Spark-based implementation for DAP-Walk where the ranking of nodes of candidate co-referents is obtained by aggregating the distributed ranks calculated across spark workers. Experimental results on benchmark datasets show the efficiency and scalability of DAP-Walk compared to several state-of-the-art instance matching approaches.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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