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Data linking over RDF knowledge graphs: A survey
Author(s) -
Assi Ali,
Mcheick Hamid,
Dhifli Wajdi
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5746
Subject(s) - computer science , matching (statistics) , relation (database) , context (archaeology) , process (computing) , task (project management) , rdf , data science , asset (computer security) , information retrieval , object (grammar) , data mining , semantic web , artificial intelligence , mathematics , programming language , paleontology , statistics , management , computer security , biology , economics
Summary Instance matching (IM) is the process of matching instances across Knowledge Bases (KBs) that refer to the same real‐world object (eg, the same person in two different KBs). Several approaches in the literature were developed to perform this process using different algorithmic techniques and search strategies. In this article, we aim to provide the rationale for IM and to survey the existing algorithms for performing this task. We begin by identifying the importance of such a process and define it formally. We also provide a new classification of these approaches depending on the “source of evidence,” which can be considered as the context information integrated explicitly or implicitly in the IM process. We survey and discuss the state‐of‐the‐art IM methods regarding the context information. We, furthermore, describe and compare different state‐of‐the‐art IM approaches in relation to several criteria. Such a comprehensive comparative study constitutes an asset and a guide for future research in IM.