Towards LLM-Based Semantic Mediation Engine

Achieving semantic interoperability is one of the most persistent challenges within and across data spaces, where diverse participants exchange data. The participants in data spaces usually adhere to standards; yet, they may use different schemas, terminologies, and conceptual models. While semantic standards and ontologies provide critical structure, they may not be sufficient on their own. These approaches require extensive manual alignment, struggle with evolving semantics, and cannot fully resolve the heterogeneity inherent particularly in large-scale cross-data spaces. This concept paper introduces the LLM-Based Semantic Mediation Engine (LSME) - a middleware layer that leverages Large Language Models (LLMs) to automate semantic alignment, ontology mapping, and query transformation. By modeling natural language, structured metadata, and contextual meaning in a unified representation space, LSME will enable dynamic, context-aware mediation in the absence of explicit schema-level correspondences. The LSME framework is designed to complement existing standards and integrate with established data space infrastructure, including Eclipse Dataspace Component (EDC). It will support multiple deployment patterns – such as sidecar, plugin, and policy-aware mediation – while preserving data sovereignty and system modularity. This conceptual paper shares the idea how LSME can serve as a scalable and adaptable foundation for semantic interoperability within and across data spaces. It bridges the gap between rigid semantic standards and the practical need of dynamic, heterogeneous data exchange, offering a forward-looking path for operationalising trusted data spaces across sectors including energy, health, manufacturing, and public services.