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

arXiv: https://arxiv.org/abs/2301.04770 · HTML: https://mlsanity.com/paper/2301.04770/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [DAME: Domain Adaptation for Matching Entities](https://mlsanity.com/paper/2204.09244/) (2022)

## Built on by (most cited first, 5 of 5)

- [AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark](https://mlsanity.com/paper/2412.06724/) (2024)
- [Unlocking Insights: Semantic Search in Jupyter Notebooks](https://mlsanity.com/paper/2402.13234/) (2024)
- [APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching](https://mlsanity.com/paper/2405.04820/) (2024)
- [T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework](https://mlsanity.com/paper/2410.00218/) (2024)
- [Leveraging large language models for efficient representation learning for entity resolution](https://mlsanity.com/paper/2411.10629/) (2024)

## Abstract (arXiv)

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.