Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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 …

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 …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to
follow diverse human instructions and perform a wide range of tasks. However, when …

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the
same real-world object. Recent advancement in KG embedding impels the advent of …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Language models, such as GPT-3 and ChatGPT, demonstrate remarkable abilities to follow
diverse human instructions and perform a wide range of tasks, using instruction fine-tuning …