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.<\/p>\n
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. We in this work take a different tack, proposing a transfer-learning approach to EM, leveraging pre-trained EM models from large-scale, production knowledge bases (KB). Specifically, for each entity-type in KB, (e.g., location, organization, people, etc.), we use rich synonymous names of known entities in the KB as training data, to pre-train type-detection and EM models for each type, using a novel hierarchical neural network architecture we develop. Given a new EM task, with little or no training data, we can either fine-tune or directly leverage pre-trained EM models, to build end-to-end, high-quality EM systems. Experiments on a variety of real EM tasks suggest that the pre-trained approach is effective and outperforms existing EM methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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