On the Asymmetrical Nature of Entity Matching Using Pre-Trained Transformers

Andrei Olar

2025

Abstract

Entity resolution (ER) is a foundational task in data integration and knowledge discovery, aimed at identifying which information refer to the same real-world entity. While ER pipelines traditionally rely on matcher symmetry (if a matches b then b matches a) this assumption is challenged by modern matchers based on pre-trained transformers, which are inherently sensitive to input order. In this paper, we investigate the asymmetric behavior of transformer-based matchers with respect to input order and its implications for end-to-end (E2E) ER. We introduce a strong asymmetric matcher that outperforms prior baselines, demonstrate how to integrate such matchers into E2E pipelines via directed reference graphs, and evaluate clustering performance across multiple benchmark datasets. Our results reveal that asymmetry is not only measurable but also materially impacts clustering quality, highlighting the need to revisit core assumptions in ER system design.

Paper Citation

in Harvard Style

Olar A. (2025). On the Asymmetrical Nature of Entity Matching Using Pre-Trained Transformers. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 208-215. DOI: 10.5220/0013672900004000

in Bibtex Style

@conference{kdir25,
author={Andrei Olar},
title={On the Asymmetrical Nature of Entity Matching Using Pre-Trained Transformers},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013672900004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - On the Asymmetrical Nature of Entity Matching Using Pre-Trained Transformers
SN -
AU - Olar A.
PY - 2025
SP - 208
EP - 215
DO - 10.5220/0013672900004000
PB - SciTePress