CORDEL: A Contrastive Deep Learning Approach for Entity Linkage

Zhengyang Wang, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Shuiwang Ji

Abstract:

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 twinnetwork 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%.