Knowledge Graphs-Driven Intelligence for Distributed Decision Systems
Rosario Napoli, Gabriele Morabito, Antonio Celesti, Massimo Villari, Maria Fazio

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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Mobile Crowdsensing and Crowdsourcing
