Distribution alignment is critical for entity matching in low-resource settings—understanding which algorithmic choices matter most helps practitioners build more reliable data integration systems with limited supervision.
This paper investigates BEACON, a method for matching records across databases when you have limited labeled data and domain knowledge. The researchers test how different design choices and data availability affect performance, revealing insights about how distribution alignment helps the system adapt to new domains.