The Role and Applications of Semantic Interoperability Tools and eXplainable AI in the Development of Smart Food Systems: Findings from a Systematic Literature Review

Abstract

Smart food systems generate vast and diverse data across the supply chain, yet inconsistent data structures and limited interoperability hinder their full potential. Achieving semantic interoperability, where systems can exchange and interpret data with shared meaning, is essential for enabling intelligent integration and decision-making. Tools such as ontologies, knowledge graphs, and reasoning engines play a key role in this process. In this paper, we refer to these as Semantic Interoperability (SI) tools: a broad category that includes technologies grounded in Semantic Web standards (e.g., RDF, OWL, SPARQL) but emphasizes their applied role in aligning meaning across heterogeneous systems. Coupled with eXplainable Artificial Intelligence (XAI), these technologies enhance transparency and trust in AI-driven decisions, such as personalized food recommendations tailored to an individual’s health conditions and preferences. This paper presents a Systematic Literature Review (SLR) examining the role of semantic interoperability tools and XAI in the development of smart food systems. Through an analysis of 39 studies, the review identifies key semantic technologies and XAI methods used in food systems, with a focus on their application in intelligent food recommendation systems. The findings reveal that while significant progress has been made, current systems often lack adequate transparency and personalization, limiting user trust and engagement. To address these gaps, the paper proposes the integration of semantic interoperability tools with XAI to create smarter, more reliable food systems. As part of this effort, the paper introduces the conceptual model for the Semantic Explainable Food Recommendation Ontology (SEFRO), a work-in-progress ontology, designed to connect entities and relationships within food systems in an intelligent manner, with the goal of enabling personalized, explainable, and interoperable food recommendations that meet the growing demands for smart food systems.