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 …

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …

Foundation Models (FMs) are models trained on large corpora of data that, at very large
scale, can generalize to new tasks without any task-specific finetuning. As these models …

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 matching (EM) finds data instances that refer to the same real-world entity. Most EM
solutions perform blocking then matching. Many works have applied deep learning (DL) to …

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 …

Sensitive personal data are created in many application domains, and there is now an
increasing demand to share, integrate, and link such data within and across organisations in …

Can AI help automate human-easy but computer-hard data preparation tasks that burden
data scientists, practitioners, and crowd workers? We answer this question by presenting …

Entity resolution (ER) is an important data integration task with a wide spectrum of
applications. The state-of-the-art solutions on ER rely on pre-trained language models …

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving
language models to improve effectiveness. This is applied to both main steps of ER, ie …