Semantic-Web-Driven Visualization and Fault-Reasoning Framework for Circuit-Oriented Systems

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Efficient and explainable reasoning over dynamic, heterogeneous data remains a key challenge for intelligent diagnostic and monitoring systems. This paper presents a unified semantic-web-based framework that integrates ontology modeling, rule-driven inference, and interactive visualization into a scalable, service-oriented architecture. The proposed system couples Web Ontology Language (OWL)-based knowledge representation with dynamic Semantic Web Rule Language (SWRL) rule execution and control-theoretic feedback, forming a closed-loop semantic reasoning cycle that continuously refines ontology and rule parameters. To ensure real-time performance, the framework employs parallelized rule evaluation, adaptive caching, and incremental inference across distributed reasoning nodes. A modular semantic query interface bridges reasoning and visualization layers, enabling transparent inspection of causal relationships and human-in-the-loop knowledge refinement. Experimental results demonstrate that the proposed system achieves sub-linear latency growth with ontology size, reduces inference delay by up to 56% through indexing-caching synergy, and maintains detection accuracy above 95% under complex fault conditions. The end-to-end latency remains below 300 ms for medium-scale ontologies, validating its suitability for real-time diagnostic, telepresence, and edge-analytics applications. These findings establish a novel synthesis between symbolic reasoning and adaptive system control, offering both computational efficiency and semantic interpretability for circuit-oriented and cyber-physical diagnostic systems, while providing a foundation that may be extended to other semantic reasoning domains.