Publication | Open Access
Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer Learning
117
Citations
57
References
2019
Year
Artificial IntelligenceEngineeringMachine LearningSemantic WebNatural Language ProcessingEntity MatchingInformation RetrievalData ScienceData MiningManagementData IntegrationOntology AlignmentData ManagementNamed-entity RecognitionEntity ResolutionFuzzy LogicVery Large DatabaseEntity DisambiguationKnowledge DiscoveryComputer ScienceEnd-to-end Fuzzy Entity-matchingPre-trained Deep ModelsRecord LinkageRelationship ExtractionTransfer LearningData Modeling
Entity matching (EM), also known as entity resolution, fuzzy join, and record linkage, refers to the process of identifying records corresponding to the same real-world entities from different data sources. It is an important and long-standing problem in data integration and data mining. So far progresses have been made mainly in the form of model improvements, where models with better accuracy are developed when large amounts of training data is available. In real-world applications we find that advanced approaches can often require too many labeled examples that is expensive to obtain, which has become a key obstacle to wider adoption.
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