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|Title:||A knowledge-based approach for duplicate elimination in data cleaning|
|Authors:||Lup Low, W.|
Li Lee, M.
Wang Ling, T.
|Citation:||Lup Low, W.,Li Lee, M.,Wang Ling, T. (2001). A knowledge-based approach for duplicate elimination in data cleaning. Information Systems 26 (8) : 585-606. ScholarBank@NUS Repository. https://doi.org/10.1016/S0306-4379(01)00041-2|
|Abstract:||Existing duplicate elimination methods for data cleaning work on the basis of computing the degree of similarity between nearby records in a sorted database. High recall can be achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision can be achieved analogously at the cost of lower recall. This is the recall-precision dilemma. We develop a generic knowledge-based framework for effective data cleaning that can implement any existing data cleaning strategies and more. We propose a new method for computing transitive closure under uncertainty for dealing with the merging of groups of inexact duplicate records and explain why small changes to window sizes has little effect on the results of the sorted neighborhood method. Experimental study with two real-world datasets show that this approach can accurately identify duplicates and anomalies with high recall and precision, thus effectively resolving the recall-precision dilemma.|
|Source Title:||Information Systems|
|Appears in Collections:||Staff Publications|
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