Federated Service for Semantic Misalignment in Supply–Demand Matching

Abstract

As digital transformation accelerates, data has become a core driver of technological innovation and economic growth. However, a key challenge in data utilization is the semantic misalignment between data supply and the demands of business scenarios. This misalignment significantly hinders efficient data flow and collaborative utilization. To address this issue, this article proposes a federated service solution integrating blockchain and decentralized autonomous organizations and operations (DAOs), large language models (LLMs) and scenarios engineering, federated learning and edge computing, as well as encryption technologies and privacy-computing. A five-layer federated service framework is introduced, consisting of the foundation layer, the data-scenario layer, the semantic coordination layer, the incentive-security layer, and the application layer, which is designed to ensure efficient and context-aware data supply–demand matching while preserving privacy and scalability. Moreover, the core mechanisms for semantic coordination are proposed, and a detailed solution process for resolving semantic misalignment with these mechanisms, as well as an illustrative example, is also presented. The proposed federated service framework offers an effective solution to semantic misalignment in supply–demand matching, fostering seamless data collaboration across diverse business scenarios. This work provides an intelligent service paradigm that leverages distributed data co-governance to address semantic challenges in the digital economy.