Next Generation Cross-Sectoral Data Platform for the Agri-Food Sector

The food system is a complex network encompassing various stakeholders, including primary producers, manufacturers, retailers, and consumers. Across all the stages of the food supply chain, a significant amount of data is produced, offering valuable insights crucial for ensuring the delivery of safe, high-quality, and cost-effective products to meet the needs of a growing global population. Recommender systems are commonly used in the food domain but often lack personalization, leading to generic recommendations. Enhancing user experience through explainability offers transparent reasoning behind
recommendations, fostering trust and informed decision-making. Semantic reasoning can be enhanced through ontology-based user profiles. Moreover, the increased data sharing in the food sector has raised privacy and security concerns, prompting the development of privacy-preserving data platforms.
This PhD project aims to address these challenges by (1) utilizing ontologies for enhancing semantic interoperability, (2) employing eXplainable Artificial Intelligence (XAI) methods and semantic reasoning for enhancing the transparency of recommender systems, and (3) designing a privacy-preserving data platform that facilitates data sharing while ensuring the protection of sensitive information.