The state-of-the-art performance on entity resolution (ER) has been achieved by deep learning. However, models usually need to be trained large quantities of accurately labeled training data, and cannot easily tuned towards a target workload. in real scenarios, there may not sufficient data; even if they are abundant, their distribution is almost certainly different from data some extent. To al...

While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends large quantities of accurately labeled training data. To alleviate data labeling burden, Active Learning (AL) presents itself as a feasible solution that focuses deemed useful for model training. Building upon recent advances in risk analysis ER, which can provide more ...

Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approach for entity that links to latent entities, where prior representation on structure exchangeable. First, adopt flexible tractable set priors structure, which corresponds special class random partition models. Second, ...

Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in absence unique identifiers. A major advancement ER methodology has been application Bayesian generative models, which provide a natural framework for inferring latent entities with rigorous quantification uncertainty. Despite these advantages, existing models are severely l...

Abstract Entity resolution, accurately identifying various representations of the same real-world entities, is a crucial part data integration systems. While existing learning-based models can achieve good performance, are extremely dependent on quantity and quality training data. In this paper, MixER model proposed to alleviate these problems. The utilizes our newly designed augmentation metho...

In Papadakis et al. (2020), we presented the latest release of JedAI, an open-source Entity Resolution (ER) system that allows for building a large variety end-to-end ER pipelines. Through thorough experimental evaluation, compared schema-agnostic pipeline based on blocks with another schema-based similarity joins. We applied them to 10 established, real-world datasets and assessed respect effe...

• Entity resolution (ER) is to determine whether or not different entity representations (e.g., records) correspond to the same real-world entity.

Entity resolution identifies semantically equivalent entities, e.g., describing the same product or customer. It is especially challenging for big data applications where large volumes of data from many sources have to be matched and integrated. Entity resolution for multiple data sources is best addressed by clustering schemes that group all matching entities within clusters. While there are m...