Integrating Semantic Models and Automated Metadata Enrichment to Advance Environmental Data Interoperability
Year: 2026
Type: conference-paper
Abstract: The Floods and Droughts Research Infrastructure (FDRI) is delivering advanced field monitoring alongside interoperable digital infrastructure to help us better manage risks from these extremes. Here we present three complementary strands of work that advance semantic standardisation and machine-readability within FDRI and the wider environmental data ecosystem. First, we introduce a site and infrastructure metadata data model designed to harmonise the description of monitoring locations and devices. The model provides a structured, extensible framework to capture key attributes of assets, supporting consistent documentation and integration across systems. Second, we describe the development of a controlled vocabulary for observed environmental properties. This vocabulary aims to standardise the representation of observed properties, enabling improved data discovery and alignment across repositories. By linking terms to persistent identifiers and semantic definitions, it supports FAIR principles and facilitates interoperability with external standards. Third, we present an exploratory study of automated approaches to decompose observed properties according to the I-ADOPT ontology, using the above-mentioned vocabulary as one use case. Observed properties are often captured as unstructured text, limiting their usefulness for discovery and interoperability. This work develops and evaluates multiple methods to extract, structure and map key components to controlled vocabulary terms. The project demonstrates pathways towards scalable enrichment of metadata within operational data services. Together, these efforts illustrate how combining data modelling, vocabulary development and automated semantic annotation can significantly enhance environmental data discoverability and reuse by supporting applications such as advanced search, cross-dataset analysis and AI-driven methods.
Author: Rawsthorne, Helen
Institution: UK Centre for Ecology & Hydrology
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204939354
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