MontoFlow – a maritime ontology framework for modeling ship sensory systems

Ivanovic, Pavle; Burbach, Simon; Maleshkova, Maria

doi:10.1007/978-3-032-09530-5_16

MontoFlow – a maritime ontology framework for modeling ship sensory systems

Publication date

2025-10-29

Document type

Conference paper

Author

Organisational unit

DOI

10.1007/978-3-032-09530-5_16

10.5281/zenodo.15390282

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/21767

Scopus ID

2-s2.0-105022009620

Conference

24th International Semantic Web Conference (ISWC 2025) ; Nara, Japan ; November 2–6, 2025

Publisher

Springer Nature Switzerland

Series or journal

Lecture Notes in Computer Science

Periodical volume

16141

Book title

The Semantic Web – ISWC 2025 : 24th International Semantic Web Conference, Nara, Japan, November 2–6, 2025, Proceedings, Part II

ISBN

978-3-032-09530-5

Part of the university bibliography

✅

Language

English

Keyword

Anomaly detection

Condition monitoring

Ontology engineering

Ontology instantiation

Semantic sensor networks

Ship maintenance

Ship sensors

Abstract

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 (https://creativecommons.org/licenses/by-nc-sa/4.0/)

Version

Published version

Access right on openHSU

Metadata only access