The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Record Linkage in Large Data Sets
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
A Fast Linkage Detection Scheme for Multi-Source Information Integration
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
A Comparison of Personal Name Matching: Techniques and Practical Issues
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Towards automated record linkage
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
A two-step classification approach to unsupervised record linkage
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Probabilistic data generation for deduplication and data linkage
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Decision models for record linkage
Data Mining
Automatic record linkage using seeded nearest neighbour and support vector machine classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Frameworks for entity matching: A comparison
Data & Knowledge Engineering
Automatic training example selection for scalable unsupervised record linkage
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Journal of Biomedical Informatics
Evaluation of entity resolution approaches on real-world match problems
Proceedings of the VLDB Endowment
Entity Resolution and Information Quality
Entity Resolution and Information Quality
Public record aggregation using semi-supervised entity resolution
Proceedings of the 13th International Conference on Artificial Intelligence and Law
A transparent and transportable methodology for evaluating Data Linkage software
Journal of Biomedical Informatics
CrowdER: crowdsourcing entity resolution
Proceedings of the VLDB Endowment
A record linkage process of a cervical cancer screening database
Computer Methods and Programs in Biomedicine
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Record or data linkage is an important enabling technology in the health sector, as linked data is a cost-effective resource that can help to improve research into health policies, detect adverse drug reactions, reduce costs, and uncover fraud within the health system. Significant advances, mostly originating from data mining and machine learning, have been made in recent years in many areas of record linkage techniques. Most of these new methods are not yet implemented in current record linkage systems, or are hidden within 'black box' commercial software. This makes it difficult for users to learn about new record linkage techniques, as well as to compare existing linkage techniques with new ones. What is required are flexible tools that enable users to experiment with new record linkage techniques at low costs. This paper describes the Febrl (Freely Extensible Biomedical Record Linkage) system, which is available under an open source software licence. It contains many recently developed advanced techniques for data cleaning and standardisation, indexing (blocking), field comparison, and record pair classification, and encapsulates them into a graphical user interface. Febrl can be seen as a training tool suitable for users to learn and experiment with both traditional and new record linkage techniques, as well as for practitioners to conduct linkages with data sets containing up to several hundred thousand records.