Matching product titles using web-based enrichment
Proceedings of the 21st ACM international conference on Information and knowledge management
Tuning large scale deduplication with reduced effort
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Exploiting user clicks for automatic seed set generation for entity matching
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th Brazilian Symposium on Human Factors in Computing Systems
A methodological approach to mining and simulating data in complex information systems
Intelligent Data Analysis
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Approximate data matching is a central problem in several data management processes, such as data integration, data cleaning, approximate queries, similarity search and so on. An approximate matching process aims at defining whether two data represent the same real-world object. For atomic values (strings, dates, etc), similarity functions have been defined for several value domains (person names, addresses, and so on). For matching aggregated values, such as relational tuples and XML trees, approaches alternate from the definition of simple functions that combine values of similarity of record attributes to sophisticated techniques based on machine learning, for example. For complex data comparison, including structured and semistructured documents, existing approaches use both structure and data for the comparison, by either considering or not considering data semantics. This survey presents terminology and concepts that base approximated data matching, as well as discusses related work on the use of similarity functions in such a subject.