Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …

Whether the goal is to estimate the number of people that live in a congressional district, to
estimate the number of individuals that have died in an armed conflict, or to disambiguate …

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 …

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 …

Entity resolution (ER) is the process of identifying records that refer to the same entities
within one or across multiple databases. Numerous techniques have been developed to …

BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed
to fine-tuning pre-trained language models and the deep pair-wise interaction. Albeit being …

Aspect-level sentiment analysis aims to identify the sentiment polarity of aspect words in
sentences. The existing research methods only focus on the grammatical dependencies …

HIV and HBV infection are both serious public health challenges. There are more than
approximately 4 million patients coinfected with HIV and HBV worldwide, and approximately …

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 …