Hippolyte, J-L; Romanchikova, M; Bevilacqua, M; Duncan, P; Hunt, S E; Toro, F G; Piette, A-S; Neumann, J (2023) Using ontologies to create machine-actionable datasets: two case studies. Metrology, 3 (1). pp. 65-80.

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Abstract

Achieving the highest levels of compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically. The work presented in this paper focuses on the metrological aspects of the practical implementation of standardised measurement data and metadata using semantic web technologies. The paper discusses the implementation of machine-actionable descriptions of Earth observation and bathymetry measurement dataset, based on two ontologies of quantities and units of measure selected for their prominence in the semantic web. The results highlight the need for not only user-friendly tools for the generation of semantic representations of research datasets and their metrological characteristics but also for the establishment of good practices within each scientific community. The selected ontologies have a good coverage of the concepts related to quantities, dimensions, systems of units and units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.

Item Type:	Article
Keywords:	metrology; machine actionability; ontologies; OM 2.0; QUDT; FAIR principles
Subjects:	Mathematics and Scientific Computing > Software Support for Metrology
Divisions:	Data Science
Identification number/DOI:	10.3390/metrology3010003
Last Modified:	04 Jul 2023 13:58
URI:	https://eprintspublications.npl.co.uk/id/eprint/9774