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Entity resolution has been an essential and well-studied task in datacleaning research for decades. Existing work has discussed the feasibility ofutilizing pre-trained language models to perform entity resolution and achievedpromising results. However, few works have discussed injecting domain knowledgeto improve the performance of pre-trained language models on entity resolutiontasks. In this study, we propose Knowledge Augmented Entity Resolution (KAER),a novel framework named for augmenting pre-trained language models withexternal knowledge for entity resolution. We discuss the results of utilizingdifferent knowledge augmentation and prompting methods to improve entityresolution performance. Our model improves on Ditto, the existingstate-of-the-art entity resolution method. In particular, 1) KAER performs morerobustly and achieves better results on "dirty data", and 2) with more generalknowledge injection, KAER outperforms the existing baseline models on thetextual dataset and dataset from the online product domain. 3) KAER achievescompetitive results on highly domain-specific datasets, such as citationdatasets, requiring the injection of expert knowledge in future work.

KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution