Adaptive name matching in information integration
Top Cited Papers
- 1 September 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Intelligent Systems
- Vol. 18 (5) , 16-23
- https://doi.org/10.1109/mis.2003.1234765
Abstract
Identifying approximately duplicate database records that refer to the same entity is essential for information integration. The authors compare and describe methods for combining and learning textual similarity measures for name matching.Keywords
This publication has 16 references indexed in Scilit:
- Adaptive duplicate detection using learnable string similarity measuresPublished by Association for Computing Machinery (ACM) ,2003
- Learning domain-independent string transformation weights for high accuracy object identificationPublished by Association for Computing Machinery (ACM) ,2002
- Learning object identification rules for information integrationInformation Systems, 2001
- Hardening soft information sourcesPublished by Association for Computing Machinery (ACM) ,2000
- Efficient clustering of high-dimensional data sets with application to reference matchingPublished by Association for Computing Machinery (ACM) ,2000
- Data integration using similarity joins and a word-based information representation languageACM Transactions on Information Systems, 2000
- Integration of heterogeneous databases without common domains using queries based on textual similarityPublished by Association for Computing Machinery (ACM) ,1998
- Learning string-edit distancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- A Theory for Record LinkageJournal of the American Statistical Association, 1969