The ocean generates vast amounts of data — for example, satellite imagery, fishing catch records, sensor readings, and maritime traffic records — but this information is fragmented, and often siloed, making it difficult to interpret, and thus becoming a barrier to sustainable governance.
This fragmentation is more than a technical hurdle; it is a governance crisis. As marine spatial planning and blue economy investments expand, semantic interoperability — the ability to unify data across domains through shared meaning — becomes urgent.
The True Cost of Fragmented Data
Like a puzzle scattered across separate rooms, ocean data is useless if its pieces cannot connect. Consider these practical examples:
In Portugal, fisheries managers often face challenges in connecting data across institutions. For example, aquaculture licenses from the Directorate-General for Natural Resources (DGRM) are stored in formats like PDFs or geospatial files, making them hard to cross-reference with real-time water quality data from the national water agency (APA) or oceanographic data from the national marine institute (IPMA), which are stored in technical formats such as NetCDF.
Ahtiainen et al. (2025) highlight the challenges of integrating heterogeneous data into large-scale environmental analyses. In the Baltic Sea, information fragmentation has required supplementing models with expert input, reinforcing the need for system interoperability to enable coherent data collection and sharing.
Open data portals alone do not solve interoperability. Traditional MOUs (Memoranda of Understanding) fail because they transfer data without translating it. Thus, semantic gaps can exist (e.g., "Water temperature" may mean surface readings (APA) or depth-averaged values (IPMA)).
Even when data and metadata are technically accessible, its usefulness depends on consistent semantics. For example, the Portuguese Hydrographic Institute (see here) provides marine geospatial data and real-time buoy data free of charge in shapefiles, Geodis, NetCDF, and CSV formats through its Hydrographic+ geoportal. However, users must manually reconcile the metadata (available in a separate catalogue) to verify licensing terms and contextual definitions.
As highlighted by Haimon et al. (2025) in Ocean Data Ecosystem, the lack of standardized ontologies — that is, clear models that define the meaning of data — forces researchers to manually adjust information, which goes against the FAIR Principles. These principles advocate that data should be easily findable, accessible, interoperable, and reusable.
This criticism reinforces the view of Tim Berners-Lee (2006), who argued for the importance of data 'speaking the same language' through semantic interoperability.
The result? Decisions are delayed, policies lack precision, and innovation stagnates. The OECD’s The Ocean Economy to 2050 report (OECD, 2025) underscores the need to enhance ocean observation data collection systems and advocates for robust public-private collaborations to improve data accessibility, interoperability, and governance.
Semantic Technologies: Making ocean data understandable
The limitations of traditional data-sharing frameworks demand a change in basic assumptions because they do not ensure that different systems can truly "understand" each other.
Here below some illustrative examples of how semantic technologies can make ocean data more useful and interoperable:
— Ontologies can bridge these gaps by enabling cross-domain interoperability. In simple terms, they act as dictionaries that define key concepts and relationships within a domain — making it possible for different systems and datasets to become comparable.
For example, the MarineTLO ontology (see here) is a domain ontology containing 91 classes and 51 properties which allows not only to express cross-categorical knowledge but also for the formulation of generic queries, allowing users to search across different data sources using shared concepts. It contains information about marine species, ecosystems, water conditions, geographic areas, and vessels.
— Beyond spatial planning, semantically harmonized datasets enhance the reliability of AI-driven tools. In the EU’s Blue Cloud Project, semantically aligned datasets are improving the reliability of AI-powered biodiversity models. By using common metadata standards and interoperable protocols like CSW, OAI-PMH, and ERDDAP, researchers can combine data from different sources more effectively, improving species distribution models for marine biodiversity monitoring
— By embedding FAIR principles into ocean governance, semantic web technologies transform fragmented data into actionable knowledge, ensuring policies evolve as swiftly as the ecosystems they protect.
Despite their potential, semantic technologies face adoption challenges:
Institutional inertia: Agencies cling to legacy systems, fearing transition costs.
Lack of incentives: Data holders see interoperability as a burden, not a value multiplier.
Skill gaps: Few marine scientists are trained in knowledge graph technologies.
Conclusion
The future of the ocean depends on a shared language for data. Semantic interoperability is not just technical jargon — it is the foundation for climate resilience, biodiversity protection, and sustainable blue growth. To realize this vision, governments must mandate standard ontologies in ocean data initiatives and invest in interoperability hubs like Blue Cloud.
However, technology alone is not enough: the true potential of FAIR principles and semantic tools can only be unlocked by tackling human and institutional barriers head-on. Only then can fragmented data become a force for systemic change.
Bibliograph References:
Ahtiainen, H., Dodd, L. F., Korpinen, S., Picolinate, K., & Saikkonen, L. (2025). Quantifying effectiveness and sufficiency of measures – An application of the DPSIR framework for the marine environment. Marine Policy, 172, 106480. https://doi.org/10.1016/j.marpol.2024.106480. Haimon, O., Lehan, Y., & Sagi, T. (2025). An overview of the ocean data ecosystem. https://doi.org/10.5194/egusphere-2025-1016. Shadbolt, N., Hall, W., & Berners-Lee, T. (2006). The semantic web revisited.
IEEE Intelligent Systems, 21(3), 96–101. https://doi.org/10.1109/MIS.2006.62
