Knowledge Graphs-Driven Intelligence for Distributed Decision Systems

2025-12-01

International Conference on Utility and Cloud Computing

AI Summary

This work addresses the challenges of coordination in distributed decision-making systems arising from data heterogeneity, dynamic environmental changes, and the absence of a centralized coordination mechanism. To this end, the authors propose a decentralized intelligent coordination approach that integrates knowledge graphs with graph embedding techniques. By leveraging GraphSAGE to iteratively aggregate local semantic information across nodes, the method constructs a dynamically evolving global semantic abstraction—referred to as a knowledge map—enabling semantically consistent and adaptive collaborative decision-making without centralized control. This study represents the first integration of knowledge graphs and graph embeddings specifically tailored for decentralized settings. Experimental results demonstrate that the proposed approach effectively maintains semantic coherence and adaptability across diverse network topologies and workload conditions, showing strong applicability in edge computing, Internet of Things (IoT), and multi-agent systems.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

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.

Problem

• Distributed Decision Systems
• Data Heterogeneity
• Decentralized Coordination
• Semantic Coherence
• Dynamic Environments

Innovation

• Knowledge Graphs
• Graph Embeddings
• Decentralized Intelligence
• GraphSAGE
• Knowledge Map