The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Entity Identification in Database Integration
Proceedings of the Ninth International Conference on Data Engineering
Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Record linkage: similarity measures and algorithms
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Example-driven design of efficient record matching queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Reasoning about record matching rules
Proceedings of the VLDB Endowment
Proceedings of the sixth ACM international conference on Web search and data mining
Tuning large scale deduplication with reduced effort
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Do We Need Entity-Centric Knowledge Bases for Entity Disambiguation?
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
Extending string similarity join to tolerant fuzzy token matching
ACM Transactions on Database Systems (TODS)
Linkage of compound objects for supporting maintenance of large-scale web sites
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define "how similar is similar" for real applications, since it is rather hard to automatically select appropriate similarity functions. In this paper we attempt to address this problem. As there are a large number of similarity functions, and even worse thresholds may have infinite values, it is rather expensive to find appropriate similarity functions and thresholds. Fortunately, we have an observation that different similarity functions and thresholds have redundancy, and we have an opportunity to prune inappropriate similarity functions. To this end, we propose effective optimization techniques to eliminate such redundancy, and devise efficient algorithms to find the best similarity functions. The experimental results on both real and synthetic datasets show that our method achieves high accuracy and outperforms the baseline algorithms.