Entity Resolution: Theory, Practice & Open Challenges

By Lise Getoor, Ashwin Machanavajjhala. 2012

358 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

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Citations in the corpus

Fast Learning from Distributed Datasets without Entity Matching (2016, background): “From another perspective, in concert with the open issuesin [13], we study ER as component of a pipeline for classification, and highlight how matching is n ot ecessary for the purpose of learning.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, methodology): “For error detection, many methods rely on violations of integrity constraints [8, 11] or duplicate [20, 29, 32] and outlier detection [15, 22] methods to identify errors.”
A Survey of Fake News (2020, background): “The task to reduce redundancy is often referred to as entity resolution [Getoor and Machanavajjhala 2012], which is also known as deduplication or record linkage [Nickel et al. 2016] (e.g., see related studies such as Altowim et al. [2014], Bhattacharya and Getoor [2007], Christen [2008], Stoerts…”
A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, methodology): “We believe that variants of our approach could also apply to pure algorithmic cleaning (e.g., various machine learned error classiﬁers [13]).”
Deep Learning for Entity Matching: A Design Space Exploration (2018, background): “Related EMWork: EM has received much attention [9, 19, 24, 55, 63].”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “Note that representing the same entity is not equivalent to representing the same string, e.g., Donald J. Trump and Donald John Trump both represent the same entity; hence, entity resolution techniques [Getoor and Machanavajjhala 2012] can be used to identify proper matchings.”
Data Integration: The Current Status and the Way Forward (2018, background): “This step is crucial in enabling comparing records across sources; (e) Deduplication and record linkage tools [4, 7, 10] are used to cluster records.”
Effective Data Cleaning with Continuous Evaluation (2016, background): “A large body of work has addressed some of the well-deﬁned problems; for example, refer to the survey [11] on cleaning relational data with violations of integrity constraints, and to the multiple surveys on record linkage and de-duplication [14, 9, 8, 15, 10], and also on data proﬁling [1].”