The project aims to design and implement a data enrichment framework that enhances the quality, completeness, and interoperability of information stored in Digital Product Passports (DPPs). DPPs are digital records containing detailed information about a product’s lifecycle — from material sourcing and manufacturing to usage, repair, and recycling. They play a vital role in enabling circular economy models, sustainability tracking, and regulatory compliance within the European Union’s green transition initiatives.
However, the effectiveness of DPPs depends heavily on the accuracy and richness of their data. Many existing product data sources are fragmented, inconsistent, or incomplete. This project focuses on data enrichment techniques — integrating, validating, and enhancing product data using machine learning, semantic modeling, and external knowledge sources such as supplier databases, environmental datasets, and certification repositories.
The main objectives are:
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To identify relevant data sources for enriching product information in DPPs.
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One of the following objectives:
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To develop a scalable data integration and enrichment pipeline.
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To apply AI-driven methods for data validation, entity matching, and/or metadata completion.
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