  Entity resolution has been an essential and well-studied task in data
cleaning research for decades. Existing work has discussed the feasibility of
utilizing pre-trained language models to perform entity resolution and achieved
promising results. However, few works have discussed injecting domain knowledge
to improve the performance of pre-trained language models on entity resolution
tasks. In this study, we propose Knowledge Augmented Entity Resolution (KAER),
a novel framework named for augmenting pre-trained language models with
external knowledge for entity resolution. We discuss the results of utilizing
different knowledge augmentation and prompting methods to improve entity
resolution performance. Our model improves on Ditto, the existing
state-of-the-art entity resolution method. In particular, 1) KAER performs more
robustly and achieves better results on "dirty data", and 2) with more general
knowledge injection, KAER outperforms the existing baseline models on the
textual dataset and dataset from the online product domain. 3) KAER achieves
competitive results on highly domain-specific datasets, such as citation
datasets, requiring the injection of expert knowledge in future work.
