Probabilistic and Semantic Matching for Context-Aware Event Notification Systems

Abstract

Event-driven communication platforms play a critical role in large-scale distributed information systems. However, conventional publish/subscribe architectures are often constrained by rigid event representations and inefficient matching mechanisms, which limits their ability to handle complex, ambiguous, and context-dependent information. This paper presents an intelligent event dissemination framework that integrates semantic modeling, fuzzy inference, and knowledge-enhanced reasoning to support flexible and accurate subscription matching. The proposed system constructs a hierarchical concept space to represent event semantics and embeds domain knowledge into an extensible reasoning layer, enabling the interpretation of implicit relationships among heterogeneous data sources. The framework adopts a dual-layer architecture consisting of an offline knowledge construction module and an online adaptive matching engine. The offline module generates structured semantic representations and uncertainty models from domain knowledge bases, while the online engine dynamically transforms event streams into semantic graphs and executes relevance-aware matching through probabilistic reasoning. Extensive evaluations demonstrate that the proposed approach significantly improves semantic expressiveness, matching precision, and system scalability compared with conventional publish/subscribe solutions. The results indicate that semantic-aware reasoning mechanisms provide an effective foundation for next-generation intelligent event notification systems.