The increasing operational demands in maritime contexts, particularly during time-sensitive missions like search and rescue, necessitate reliable, intelligent support systems. These systems depend on semantically structured and interoperable models to integrate and interpret complex sensor data as well as facilitate informed decision-making. We introduce MontoFlow, a semantic integration framework that combines dynamic data access with domain-specific knowledge representation. It links static properties with dynamic sensory measurements, forming the foundation for advanced maritime diagnostics. At its core, MontoFlow incorporates the SHIP Ontology, a maritime-focused SSN/SOSA extension that provides a comprehensive semantic model describing onboard sensors, vessel components, and their observations. We illustrate the practical relevance and rationale behind the development of MontoFlow through real-world examples, with emphasis on ship maintenance and onboard anomaly detection. The SHIP Ontology is thoroughly evaluated based on domain coverage and a use case in the maritime context, demonstrating both high quality and practical applicability. This work presents a reusable and extensible resource for semantically enriching maritime sensory data, supporting advanced analytics and dynamic data monitoring. Ontology: https://burbachs.github.io/ShipSensoryOntology/SHIP.owl GitHub: https://github.com/BurbachS/ShipSensoryOntology License: CC BY-NC-SA 4.0 DOI: 10.5281/zenodo.15390282