Knowledge Graphs-Driven Intelligence for Distributed Decision Systems

TL;DR

This paper presents a novel decentralized knowledge-sharing framework using Knowledge Graphs and Graph Embeddings to enhance decision-making in distributed systems, validated through extensive simulations.

Contribution

It introduces the Knowledge Sharing paradigm leveraging semantic graphs and embeddings for decentralized intelligence in dynamic environments.

Findings

Effective maintenance of semantic coherence across nodes.

Adaptability demonstrated in complex, dynamic scenarios.

Suitable for Edge Computing, IoT, and multi-agent systems.

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.