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 …

Trajectory-based applications have acquired significant attention over the past decade with
the rising size of trajectory data generated by users. However, building trajectory-based …

Entity resolution, a longstanding problem of data cleaning and integration, aims at
identifying data records that represent the same real-world entity. Existing approaches treat …

Entity Matching (EM) aims to identify whether two tuples refer to the same real-world entity
and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as …

Rule discovery from large datasets is often prohibitively costly. The problem becomes more
staggering when the rules are collectively defined across multiple tables. To scale with large …

Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented
context: information describing a single entity can be scattered across multiple datasets or …

A geospatial database is today at the core of an ever increasing number of services.
Building and maintaining it remains challenging due to the need to merge information from …

This paper studies two questions about rule discovery. Can we characterize the usefulness
of rules using quantitative criteria? How can we discover rules using those criteria? As a …

The rapid growth in published clinical trials makes it difficult to maintain up-to-date
systematic reviews, which requires finding all relevant trials. This leads to policy and practice …