Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies
Abstract
:1. Introduction
- automate the processing of numerical quantities while ensuring correct dimensional analysis in programming languages [6],
- unify the understanding of “physical dimensions, units of measure, functions of quantities, and dimensionless quantities” across engineering systems [7],
- optimise the conversion of equivalent symbolic combinations of units [8], and
- more generally provide formal specifications of coherent systems of units and dimensions [9].
2. Materials and Methods
2.1. Selected Semantic Representations
- The Ontology of units of Measure (OM) 2.0, version 2.0.38 released on 4 April 2022;
- Quantities, Units, Dimensions and Data Types (QUDT) ontologies, version 2.1.19 released on 2 August 2022.
2.2. Evaluation Criteria
- The coverage of the metrology concepts by each ontology, assessing how the two ontologies fulfil the requirements of both case studies in terms of semantic representation of metrological data;
- Machine-actionability and compliance with the FAIR principles, assessing how the two ontologies facilitate automated processing, interoperability, and reusability of the datasets;
- Alignment with the “M-layer”, assessing how the two ontologies comply with the semantically consistent triplet of information (where q is the value, is the unit and specifies the semantics of the quantity) [28]; and
- Traceability to standards, understanding how they link with official/authoritative/normative resources.
2.3. Dataset 1: Earth Observation
2.4. Dataset 2: Bathymetry
2.5. Implementation Workflow
- the context in which the dataset was created (provenance);
- the devices that were used to make the measurements; and
- the measurement results.
- queries that are independent from the ontologies of units of measurement;
- queries specific to OM 2.0; and
- queries specific to QUDT.
3. Results
3.1. Earth Observation Semantic Representation
3.2. Bathymetric Data Semantic Representation
4. Discussion
4.1. Context Information Retrieval
4.2. Machine-Actionability of Metrology Concepts
4.3. Limitations of the Results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AATSR | Advanced Along Track Scanning Radiometer |
AVHRR | Advanced Very High-Resolution Radiometer |
BIPM | International Bureau of Weights and Measures |
CIPM | International Committee for Weights and Measures |
CODATA | Committee on Data of the International Science Council |
DRUM | Digital Representation of Units of Measurement |
FAIR | Findable, Accessible, Interoperable, Reusable |
FCDR | Fundamental Climate Data Record |
FIP | FAIR Implementation Profile |
IEC | International Electrotechnical Commission |
ISO | International Organization for Standardization |
M4M | Metadata for Machines |
M-layer | Metrological Layer |
MBES | MultiBeam EchoSounder |
NASA | National Aeronautics and Space Administration |
NIST | National Institute of Standards and Technology |
NOAA | National Oceanic and Atmospheric Administration |
NMI | National Metrology Institute |
OM 2.0 | Ontology of units of Measure version 2.0 |
OWL | the OWL 2 Web Ontology Language |
PROV-O | PROVenance Ontology |
QUDT | Quantities, Units, Dimensions and Data Types ontologies |
RDM | Research Data Management |
SI | International System of Units |
SSN | Semantic Sensor Network |
UML | Unified Modeling Language |
WGS84 | World Geodetic System |
XML | Extensible Markup Language |
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OM 2.0 entity | QUDT entity | ||
---|---|---|---|
Physical quantities | Space count average | Number class | Dimensionless, instance of QuantityKind class |
ICT count average | |||
Earth count | |||
ICT radiance | Radiance class | Radiance, instance of QuantityKind class | |
Instrument temperature | ThermodynamicTemperature class | ThermodynamicTemperature, instance of QuantityKind class | |
Latitude | EclipticLatitude class | ||
Longitude | EclipticLongitude class | AngularDistance, instance of QuantityKind class | |
Sun-Zenith angle | |||
Satellite-Zenith angle | Angle class | Angle, instance of QuantityKind class | |
Units of measurement | counts | one, instance of PrefixedUnit class | NUM, instance of CountingUnit class, applicable unit of the Dimensionless quantity kind |
∘ | degree, instance of Unit class | DEG, instance of Unit class, applicable unit of the AngularDistance quantity kind | |
K | kelvin, instance of TemperatureUnit class | K, instance of Unit class, applicable unit of the ThermodynamicTemperature quantity kind | |
Wm sr | wattPerSquareMetreSteradian, instance of RadianceUnit class | W_PER_M2_SR, instance of Unit class, applicable unit of the Radiance quantity kind | |
Metrology | Systematic uncertainty | n/a | standardUncertainty property |
Random uncertainty | n/a | standardUncertainty property | |
Class of observed quantity error correlation | n/a | n/a | |
Error correlation matrix | n/a | n/a |
OM 2.0 entity | QUDT entity | ||
---|---|---|---|
Physical quantities | Depth | Depth class | Depth, instance of QuantityKind class |
x distance to datum | Distance class | Distance, instance of QuantityKind class | |
y distance to datum | Radiance class | ||
Units of measurement | metre | metre, instance of LengthUnit class | M, instance of Unit class, applicable unit of the Length quantity kind |
Metrology | Measurement uncertainty | n/a | standardUncertainty property |
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Hippolyte, J.-L.; Romanchikova, M.; Bevilacqua, M.; Duncan, P.; Hunt, S.E.; Grasso Toro, F.; Piette, A.-S.; Neumann, J. Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies. Metrology 2023, 3, 65-80. https://doi.org/10.3390/metrology3010003
Hippolyte J-L, Romanchikova M, Bevilacqua M, Duncan P, Hunt SE, Grasso Toro F, Piette A-S, Neumann J. Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies. Metrology. 2023; 3(1):65-80. https://doi.org/10.3390/metrology3010003
Chicago/Turabian StyleHippolyte, Jean-Laurent, Marina Romanchikova, Maurizio Bevilacqua, Paul Duncan, Samuel E. Hunt, Federico Grasso Toro, Anne-Sophie Piette, and Julia Neumann. 2023. "Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies" Metrology 3, no. 1: 65-80. https://doi.org/10.3390/metrology3010003