SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

By Wen-Syan Li, Chris Clifton. Data & Knowledge Engineering, 2000

441 citations recorded by the source corpus.

How researchers see this paper

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Similarity flooding: a versatile graph matching algorithm and its application to schema matching (2002, background): “Previously proposed metrics for measuring the matching accuracy [11, 6] did not consider the extra work caused by wrong match proposals.”
iMAP: discovering complex semantic matches between database schemas (2004, background): “(e.g.,[21,2,22,9,17,19, 15 ,3,1,16,13],seealso[24]fora survey of other works in databases and AI).”
Data Cleaning: Problems and Current Approaches. (2000, background): “Moreover, it can effectively contribute to identify attribute correspondences between source schemas (schema matching), based on which automatic data transformations can be derived [20][9].”
An interactive clustering-based approach to integrating source query interfaces on the deep Web (2004, methodology): “Our bridging effect observation is also motivated in part by the works done in [10, 12, 16], in particular by the holistic approach to interface matching in [10].”
Comparison of Schema Matching Evaluations (2002, methodology): “In [12, 13], was evaluated with two further match tasks.”
Reconciling schemas of disparate data sources: a machine-learning approach (2001, methodology): “In the learner-based approach, the Semint system [16] uses a neural-network learner.”