Febrl: a freely available record linkage system with a graphical user interface

By Peter Christen. 2008

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Citations in the corpus

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, methodology): “ether a customer will default on a loan. We call this benchmark dataset PACS (PI Augmented Credit Scoring): realistic PIs, potentially missing and with typos, are generated for the entities by Febrl [Christen, 2008]. The data is then split vertically in two halves of the 12 original features. The si”
Evaluation of entity resolution approaches on real-world match problems (2010, methodology): “- FEBRL [9] (Freely Extensible Biomedical Record Linkage) provides a support vector machine (SVM) implementation for learning suitable matcher combinations.”
Frameworks for entity matching: A comparison (2010, methodology): “FEBRL (Freely Extensible Biomedical Record Linkage): FEBRL [18,17] is a hybrid framework supporting a training-based numerical combination approach utilizing the SVM as well as numerical approaches without training.”
A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “Data Cleaning: There have been many studies on various data-cleaning techniques, such as rule-based approaches [17, 23], outlier detection [18,31], filling missing values [48,52], and duplicate detection [9,12].”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “Most of the existing work on deduplication focuses on record matching; record matching outputs candidate pairs of duplicate records by identifying pairs of similar records [42], [43], [44], [38], which may not be transitively closed.”
SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “Data Cleaning: There have been many studies on various data-cleaning techniques, such as rule-based approaches [21, 18], outlier detection [26, 19], ﬁlling missing values, and duplicate detection [13, 9, 44].”
CrowdER: Crowdsourcing Entity Resolution (2012, methodology): “For example, some approaches model entity resolution as a classiﬁcation problem, training a classiﬁer to distinguish be-tween “duplicate” or “non-duplicate” pairs [4,6].”