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Employing trainable string similarity metrics for information integration (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bilenko, Mikhail Mooney, Raymond J. |
| Description | The problem of identifying approximately duplicate objects in databases is an essential step for the information integration process. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. |
| File Format | |
| Language | English |
| Publisher Date | 2003-01-01 |
| Publisher Institution | In Proceedings of the IJCAI Workshop on Information Integration on the Web |
| Access Restriction | Open |
| Subject Keyword | Duplicate Detection Information Integration Process Duplicate Object Distance Metric Information Integration Textual Similarity Trainable String Similarity Metric Essential Step Potential Duplicate Trainable Measure |
| Content Type | Text |
| Resource Type | Article |
