  Entity linkage (EL) is a critical problem in data cleaning and integration.
In the past several decades, EL has typically been done by rule-based systems
or traditional machine learning models with hand-curated features, both of
which heavily depend on manual human inputs. With the ever-increasing growth of
new data, deep learning (DL) based approaches have been proposed to alleviate
the high cost of EL associated with the traditional models. Existing
exploration of DL models for EL strictly follows the well-known twin-network
architecture. However, we argue that the twin-network architecture is
sub-optimal to EL, leading to inherent drawbacks of existing models. In order
to address the drawbacks, we propose a novel and generic contrastive DL
framework for EL. The proposed framework is able to capture both syntactic and
semantic matching signals and pays attention to subtle but critical
differences. Based on the framework, we develop a contrastive DL approach for
EL, called CorDEL, with three powerful variants. We evaluate CorDEL with
extensive experiments conducted on both public benchmark datasets and a
real-world dataset. CorDEL outperforms previous state-of-the-art models by 5.2%
on public benchmark datasets. Moreover, CorDEL yields a 2.4% improvement over
the current best DL model on the real-world dataset, while reducing the number
of training parameters by 97.6%.
