## Adaptive Duplicate Detection Using Learnable String Similarity Measures (2003)

Venue: | In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003 |

Citations: | 238 - 11 self |

### BibTeX

@INPROCEEDINGS{Bilenko03adaptiveduplicate,

author = {Mikhail Bilenko and Raymond J. Mooney},

title = {Adaptive Duplicate Detection Using Learnable String Similarity Measures},

booktitle = {In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003},

year = {2003},

pages = {39--48}

}

### Years of Citing Articles

### OpenURL

### Abstract

The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. 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. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.

### Citations

9023 | The Nature of Statistical Learning Theory
- Vapnik
(Show Context)
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4288 | A tutorial on hidden markov models and selected applications in speech recognition
- Rabiner
- 1989
(Show Context)
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- Witten, Frank
- 2005
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- Baeza-Yates, Ribeiro-Neto
- 1999
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1448 |
Making large-scale SVM learning practical
- Joachims
- 1999
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1416 |
A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins
- Needleman, Wunsch
- 1970
(Show Context)
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901 |
Algorithms on Strings, Trees and Sequences
- Gusfield
- 1997
(Show Context)
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831 | Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
- Durbin, Eddy, et al.
- 1998
(Show Context)
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708 | Probablistic outputs for support vector machines and comparisons to regularized likelihood methods
- Platt
- 1999
(Show Context)
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684 | Transductive inference for text classification using support vector machines
- Joachims
- 1999
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403 |
A theory for record linkage
- Fellegi, Sunter
- 1969
(Show Context)
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300 | The Merge/Purge Problem for Large Databases
- Hernandez, Stolfo
- 1995
(Show Context)
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260 | Efficient clustering of high-dimensional data sets with application to reference matching
- McCallum, Nigam, et al.
- 2000
(Show Context)
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218 | The state of record linkage and current research problems
- Winkler
- 1999
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196 | Learning string-edit distance
- Ristad, Yianilos
- 1998
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- A, Elkan
- 1997
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153 | The Field Matching Problem: Algorithms and Applications
- Monge, Elkan
- 1996
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- Cook, Holder
- 1994
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Automatic linkage of vital records
- Newcombe, Kennedy, et al.
- 1959
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- 1999
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- 2000
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