Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

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 matching (EM) identifies data instances that refer to the same real-world entity, such
as (David Smith, UWMadison) and (DM Smith, UWM). This problem has been a long …

Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of
the research examines ways for improving its effectiveness and time efficiency. The initial …

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA)
results on ER are achieved by deep learning (DL) based methods, trained with a lot of …

Entity resolution (ER) aims to identify data records referring to the same real-world entity.
Most existing ER approaches rely on the assumption that the entity records to be resolved …

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

Entity matching (EM) is a challenging problem studied by different communities for over half
a century. Algorithmic fairness has also become a timely topic to address machine bias and …