AISHIP: an ontology for extended vessel representation and multimodal data integration
Publication date
2025-11-13
Secondary publication date
2025-12-12
Document type
Conference paper
Author
Organisational unit
Conference
16th Workshop on Ontology Design and Patterns and the 1st Workshop on Bridging Hybrid Intelligence and the Semantic Web (WOP-HAIBRIDGE 2025) co-located with the 24th International Semantic Web Conference (ISWC 2025) ; Nara, Japan ; November 2–3, 2025
Publisher
RWTH
Series or journal
CEUR Workshop Proceedings
ISSN
Periodical volume
4093
Book title
WOP-HAIBRIDGE 2025 : joint proceedings WOP and HAIBRIDGE 2025
First page
18
Last page
30
Peer-reviewed
✅
Part of the university bibliography
✅
File(s)
Language
English
Abstract
The maritime sector generates vast amounts of heterogeneous data due to dense global vessel traffic. This data offers significant potential for improving operational efficiency and ensuring safety at sea. However, its effective use is hindered by a lack of semantic alignment between diverse information sources. To address this challenge, we present AISHIP, an ontology designed to unify and semantically enrich maritime data. AISHIP extends the existing VesselAI ontology by incorporating enhanced vessel characteristics, additional trajectory information, multimodal vessel representations, and a detailed conceptualization of propulsion systems. The primary objective of AISHIP is to serve as a semantic interface that facilitates the integration of heterogeneous maritime datasets, enabling consistent annotation, querying, and reasoning across systems.
By providing a shared semantic foundation, AISHIP supports a range of maritime applications, including vessel behavior analysis, fleet management, and search and rescue operations. These tasks benefit from harmonized data representations, which improve analytical precision and operational decision-making. We discuss relevant use cases to illustrate the ontology’s practical value and evaluate its design to demonstrate its quality. AISHIP represents an extensible and reusable resource, aligning with FAIR principles, and offers strong potential for adoption in future maritime analytics and digital twin frameworks.
By providing a shared semantic foundation, AISHIP supports a range of maritime applications, including vessel behavior analysis, fleet management, and search and rescue operations. These tasks benefit from harmonized data representations, which improve analytical precision and operational decision-making. We discuss relevant use cases to illustrate the ontology’s practical value and evaluate its design to demonstrate its quality. AISHIP represents an extensible and reusable resource, aligning with FAIR principles, and offers strong potential for adoption in future maritime analytics and digital twin frameworks.
Description
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
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Published version
Access right on openHSU
Open access