# Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks

arXiv: https://arxiv.org/abs/2309.11046 · HTML: https://mlsanity.com/paper/2309.11046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Modeling Voting for System Combination in Machine Translation](https://mlsanity.com/paper/2007.06943/) (2020)
- [End-to-End Entity Resolution for Big Data: A Survey](https://mlsanity.com/paper/1905.06397/) (2019)
- [A Comparison of Blocking Methods for Record Linkage](https://mlsanity.com/paper/1407.3191/) (2014)
- [Reuse and Adaptation for Entity Resolution through Transfer Learning](https://mlsanity.com/paper/1809.11084/) (2018)

## Built on by (most cited first, 1 of 1)

- [Leveraging Language Models for Automated Patient Record Linkage](https://mlsanity.com/paper/2504.15261/) (2025)

## Abstract (arXiv)

Across various domains, data from different sources such as Baidu Baike and Wikipedia often manifest in distinct forms. Current entity matching methodologies predominantly focus on homogeneous data, characterized by attributes that share the same structure and concise attribute values. However, this orientation poses challenges in handling data with diverse formats. Moreover, prevailing approaches aggregate the similarity of attribute values between corresponding attributes to ascertain entity similarity. Yet, they often overlook the intricate interrelationships between attributes, where one attribute may have multiple associations. The simplistic approach of pairwise attribute comparison fails to harness the wealth of information encapsulated within entities.To address these challenges, we introduce a novel entity matching model, dubbed Entity Matching Model for Capturing Complex Attribute Relationships(EMM-CCAR),built upon pre-trained models. Specifically, this model transforms the matching task into a sequence matching problem to mitigate the impact of varying data formats. Moreover, by introducing attention mechanisms, it identifies complex relationships between attributes, emphasizing the degree of matching among multiple attributes rather than one-to-one correspondences. Through the integration of the EMM-CCAR model, we adeptly surmount the challenges posed by data heterogeneity and intricate attribute interdependencies. In comparison with the prevalent DER-SSM and Ditto approaches, our model achieves improvements of approximately 4% and 1% in F1 scores, respectively. This furnishes a robust solution for addressing the intricacies of attribute complexity in entity matching.