Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The nature of statistical learning theory
The nature of statistical learning theory
Efficient clustering of high-dimensional data sets with application to reference matching
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning domain-independent string transformation weights for high accuracy object identification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Joint deduplication of multiple record types in relational data
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A Graph Partitioning Approach to Entity Disambiguation Using Uncertain Information
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
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Expert Systems with Applications: An International Journal
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WISE'07 Proceedings of the 8th international conference on Web information systems engineering
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This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments.