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Catalog integration of heterogeneous and volatile product data

  • The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.

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Metadaten
Author:Oliver Schmidts, Bodo Kraft, Marvin Winkens, Albert Zündorf
DOI:https://doi.org/10.1007/978-3-030-83014-4_7
ISBN:978-3-030-83013-7
Parent Title (English):DATA 2020: Data Management Technologies and Applications
Publisher:Springer
Place of publication:Cham
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Date of first Publication:2021/07/23
Date of the Publication (Server):2021/09/16
First Page:134
Last Page:153
Note:
International Conference on Data Management Technologies and Applications, DATA 2020, 7-9 July
Link:https://doi.org/10.1007/978-3-030-83014-4_7
Zugriffsart:bezahl
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik