Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

We present Ditto, a novel entity matching system based on pre-trained Transformer-based
language models. We fine-tune and cast EM as a sequence-pair classification problem to …

Entity resolution (ER) is a key data integration problem. Despite the efforts in 70+ years in all
aspects of ER, there is still a high demand for democratizing ER-humans are heavily …

Entity resolution (ER) is the task of identifying different representations of the same real-
world entities across databases. It is a key step for knowledge base creation and text mining …

Entity Resolution (ER) links entities that refer to the same real-world entity from different
sources. Existing work usually takes pairs of entities as input and judges those pairs …

We study the problem of large-scale social identity linkage across different social media
platforms, which is of critical importance to business intelligence by gaining from social data …

Crowdsourcing is widely accepted as a means for resolving tasks that machines are not
good at. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no …

Entity matching (EM) is a critical part of data integration. We study how to synthesize entity
matching rules from positive-negative matching examples. The core of our solution is …

String similarity search and join are two important operations in data cleaning and
integration, which extend traditional exact search and exact join operations in databases by …