Handling Structural Variability in Pub/Sub Systems via Ontology-Based Reasoning

Abstract

Traditional publish/subscribe middleware relies primarily on syntactic filtering mechanisms, which restrict their applicability in domains characterized by ambiguous and evolving data schemas. A semantic matching approach based on ontology-guided similarity measurement and rule-based inference was developed to address this limitation. Events and subscriptions are represented as lightweight concept graphs, and matching decisions are derived from weighted semantic distances. Experiments conducted on industrial monitoring datasets indicate improved recall in complex scenarios while maintaining acceptable processing latency. The approach proves particularly effective in environments where event structures change frequently.