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Using LLMs for the extraction and normalization of product attribute values
Brinkmann, Alexander
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Baumann, Nick
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Bizer, Christian
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DOI:
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https://doi.org/10.1007/978-3-031-70626-4_15
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URL:
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https://link.springer.com/chapter/10.1007/978-3-03...
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2024
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Buchtitel:
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Advances in databases and information systems : 28th European Conference, ADBIS 2024, Bayonne, France, August 28-31, 2024 ; Proceedings
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Titel einer Zeitschrift oder einer Reihe:
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Lecture Notes in Computer Science
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Band/Volume:
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14918
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Seitenbereich:
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217-230
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Veranstaltungstitel:
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28th European Conference on Advances in Databases and Information Systems (ADBIS 2024)
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Veranstaltungsort:
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Bayonne, France
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Veranstaltungsdatum:
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28.-31.08.2024
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Herausgeber:
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Tekli, Joe
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Gamper, Johann
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Chbeir, Richard
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Manolopoulos, Yannis
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Ort der Veröffentlichung:
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Berlin [u.a.]
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Verlag:
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Springer
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ISBN:
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978-3-031-70628-8 , 978-3-031-70626-4
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ISSN:
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0302-9743 , 1611-3349
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems V: Web-based Systems (Bizer 2012-)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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information extraction , value normalization , Large Language Models
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Abstract:
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Product offers on e-commerce websites often consist of a product title and a textual product description. In order to enable features such as faceted product search or to generate product comparison tables, it is necessary to extract structured attribute-value pairs from the unstructured product titles and descriptions and to normalize the extracted values to a single, unified scale for each attribute. This paper explores the potential of using large language models (LLMs), such as GPT-3.5 and GPT-4, to extract and normalize attribute values from product titles and descriptions. We experiment with different zero-shot and few-shot prompt templates for instructing LLMs to extract and normalize attribute-value pairs. We introduce the Web Data Commons - Product Attribute Value Extraction (WDC-PAVE) benchmark dataset for our experiments. WDC-PAVE consists of product offers from 59 different websites which provide schema.org annotations. The offers belong to five different product categories, each with a specific set of attributes. The dataset provides manually verified attribute-value pairs in two forms: (i) directly extracted values and (ii) normalized attribute values. The normalization of the attribute values requires systems to perform the following types of operations: name expansion, generalization, unit of measurement conversion, and string wrangling. Our experiments demonstrate that GPT-4 outperforms the PLM-based extraction methods SU-OpenTag, AVEQA, and MAVEQA by 10%, achieving an F1-score of 91%. For the extraction and normalization of product attribute values, GPT-4 achieves a similar performance to the extraction scenario, while being particularly strong at string wrangling and name expansion.
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