Comparative Analysis of Entity Matching Approaches for Product Taxonomy Integration

Michel Hagenah, Michaela Kümpel

2025

Abstract

This work examines different approaches to solving the entity matching problem for product categories by converting the GS1 Global Product Categorization (GPC) published by GS1 as an ontology and linking it to the Product Knowledge Graph (ProductKG). For the implementation, methods were developed in Python for word embeddings, WordNet, lemmatization, and large language models (LLMs), which then link classes of the GPC ontology with the classes of the ProductKG. All approaches were carried out on the same source data and each provided an independent version of the linked GPC ontology. As part of the evaluation, the quantities of linked class pairs were analyzed and precision, recall, and F1 score for the Food / Breakfast segment of the GS1 GPC taxonomy were calculated. The results show that no single approach is universally superior. LLMs achieved the highest F1-score due to their deep semantic understanding but suffered from lower precision, making them suitable for applications requiring broad coverage. Lemmatization achieved perfect precision, making it ideal for use cases where false matches must be avoided, though at the cost of significantly lower recall. WordNet offered a balanced trade-off between precision and recall, making it a reasonable default choice. Word embeddings, however, performed poorly in both metrics and did not outperform the other methods.

Paper Citation

in Harvard Style

Hagenah M. and Kümpel M. (2025). Comparative Analysis of Entity Matching Approaches for Product Taxonomy Integration. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-769-6, SciTePress, pages 40-51. DOI: 10.5220/0013711700004000

in Bibtex Style

@conference{keod25,
author={Michel Hagenah and Michaela Kümpel},
title={Comparative Analysis of Entity Matching Approaches for Product Taxonomy Integration},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2025},
pages={40-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013711700004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Comparative Analysis of Entity Matching Approaches for Product Taxonomy Integration
SN - 978-989-758-769-6
AU - Hagenah M.
AU - Kümpel M.
PY - 2025
SP - 40
EP - 51
DO - 10.5220/0013711700004000
PB - SciTePress