Modern distributed decision-making systems face significant challenges arising from data heterogeneity, dynamic environments, and the need for decentralized coordination. This paper introduces the Knowledge Sharing paradigm as an innovative approach that uses the semantic richness of Knowledge Graphs (KGs) and the representational power of Graph Embeddings (GEs) to achieve decentralized intelligence. Our architecture empowers individual nodes to locally construct semantic representations of their operational context, iteratively aggregating embeddings through neighbor-based exchanges using GraphSAGE. This iterative local aggregation process results in a dynamically evolving global semantic abstraction called Knowledge Map, enabling coordinated decision-making without centralized control. To validate our approach, we conduct extensive experiments under a distributed resource orchestration use case. We simulate different network topologies and node workloads, analyzing the local semantic drift of individual nodes. Experimental results confirm that our distributed knowledge-sharing mechanism effectively maintains semantic coherence and adaptability, making it suitable for complex and dynamic environments such as Edge Computing, IoT, and multi-agent systems.

翻译：现代分布式决策系统面临着数据异构性、动态环境以及去中心化协调需求带来的重大挑战。本文提出知识共享范式作为一种创新方法，该方法利用知识图谱的语义丰富性和图嵌入的表征能力来实现去中心化智能。我们的架构使各个节点能够本地构建其操作上下文的语义表示，并通过基于GraphSAGE的邻居交换迭代聚合嵌入向量。这种迭代式局部聚合过程产生了一个动态演化的全局语义抽象，称为知识图谱，从而在无需集中控制的情况下实现协调决策。为验证所提方法，我们在分布式资源编排应用场景下进行了大量实验。我们模拟了不同的网络拓扑和节点工作负载，分析了各个节点的局部语义漂移。实验结果证实，我们的分布式知识共享机制能有效维持语义一致性和适应性，使其适用于边缘计算、物联网和多智能体系统等复杂动态环境。