A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Project

Computer Science > Databases [Submitted on 27 Feb 2026] A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Project Erik Johan Nystad, Francisco Martín-Recuerda Semantic data harmonisation is a central requirement in the ILIAD project, where heterogeneous environmental data must be harmonised according to the Ocean Information Model (OIM), a modular family of ontologies for enabling the implementation of interoperable Digital Twins of the Ocean. Existing approaches to Semantic Data Harmonisation, such as RML and OTTR, offer valuable abstractions but require extensive knowledge of the technical intricacies of the OIM and the Semantic Web standards, including namespaces, IRIs, OWL constructors, and ontology design patterns. Furthermore, RML and OTTR oblige practitioners to learn specialised syntaxes and dedicated tooling. Data scientists in ILIAD have found these approaches overly cumbersome and have therefore expressed the need for a solution that abstracts away these technical details while remaining seamlessly integrated into their Python-based environments. To address these requirements, we have developed a Pythonic functional approach to semantic data harmonisation that enables users to produce correct RDF through simple function calls. The functions, structured as Python libraries, encode the design patterns of the OIM and are organised across multiple levels of abstraction. Low-level functions directly expose OWL and RDF syntax, mid-level functions encapsulate ontology design patterns, and high-level domain-specific functions orchestrate data harmonisation tasks by invoking mid-level functions. According to feedback from ILIAD data scientists, this approach satisfies their requirements and substantially enhances their ability to participate in harmonisation activities. In this paper, we present the details of our Pythonic functional approach to semantic data harmonisation and demonstrate its applicability within the ILIAD Aquaculture pilot. Comments: 17 pages, 9 figures Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2604.13042 [cs.DB]   (or arXiv:2604.13042v1 [cs.DB] for this version)   https://doi.org/10.48550/arXiv.2604.13042 Focus to learn more Submission history From: Erik Nystad [view email] [v1] Fri, 27 Feb 2026 16:49:19 UTC (6,028 KB) Access Paper: HTML (experimental) view license Current browse context: cs.DB < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)