  Multi-source entity linkage focuses on integrating knowledge from multiple
sources by linking the records that represent the same real world entity. This
is critical in high-impact applications such as data cleaning and user
stitching. The state-of-the-art entity linkage pipelines mainly depend on
supervised learning that requires abundant amounts of training data. However,
collecting well-labeled training data becomes expensive when the data from many
sources arrives incrementally over time. Moreover, the trained models can
easily overfit to specific data sources, and thus fail to generalize to new
sources due to significant differences in data and label distributions. To
address these challenges, we present AdaMEL, a deep transfer learning framework
that learns generic high-level knowledge to perform multi-source entity
linkage. AdaMEL models the attribute importance that is used to match entities
through an attribute-level self-attention mechanism, and leverages the massive
unlabeled data from new data sources through domain adaptation to make it
generic and data-source agnostic. In addition, AdaMEL is capable of
incorporating an additional set of labeled data to more accurately integrate
data sources with different attribute importance. Extensive experiments show
that our framework achieves state-of-the-art results with 8.21% improvement on
average over methods based on supervised learning. Besides, it is more stable
in handling different sets of data sources in less runtime.
