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Conference Papers Year : 2024

Knowledge Graphs for Data Integration in Retail


Semantic web technologies are widely recognized for their utility in facilitating data integration tasks. While theoretical foundations have been extensively explored, few studies have displayed their practical implementation on real-world use cases and provided feedback on their scalability. This papers aims to address this gap by introducing a complete data integration framework tailored for Bimedia, a retail company. The framework is based on an integrated architecture of semantic layers and Knowledge Graphs (KGs), aiming to enhance data interoperability and provide a deeper understanding of retail dynamics by unveiling hidden relationships captured by the data. We present empirical evaluations of various architectural implementations, supported by quantitative analyses, to guide industry practitioners in effective decision making. Furthermore, the case study of Bimedia showcases the practical application of knowledge graphs and semantic layers in the retail sector, bridging the gap between theory and practice. This study not only tackles Bimedia’s specific challenges but also provides broader insights into the evolving landscape of retail technology.
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Friday, November 22, 2024
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hal-04582725 , version 1 (24-05-2024)




  • HAL Id : hal-04582725 , version 1


Maxime Perrot, Mickaël Baron, Brice Chardin, Stéphane Jean. Knowledge Graphs for Data Integration in Retail. 27th International symposium on methodologies for intelligent systems (ISMIS 2024), Jun 2024, Poitiers - Futuroscope, France. ⟨hal-04582725⟩
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