## 4 Implementation of an Open Data Platform in Accordance with the Proposed Approach
### Introducing Hazelnut Trait Ontology
Considering the presence of irrelevant sensor measurement data types within any WSN, it is reasonable to use these mapping rules for combining data from different sources in a single and unified view. The established WSN, which was introduced within the previous section of this study, uses seven sensor measurement data types corresponding to the relevant hazelnut trait ontology classes. Table 5 presents sensor measurement data types, abbreviations, data units and class axioms, which are used while creating mapping rules. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **Abbreviation** & **Data Unit** & **Class Axiom** \\ \hline Light intensity & LI & lux & \begin{tabular}{c} :Light rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Weather temperature & WT & \({}^{\circ}\)C & \begin{tabular}{c} :Temperature rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Weather humidity & WH & \% & \begin{tabular}{c} :Relative Humidity rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Pressure & PR & Pa & \begin{tabular}{c} :Atmospheric Pressure rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Altitude & AL & m & \begin{tabular}{c} :ElevationOfCollectingSite rdf:type owl:Class; \\ rdfs:subClassOf:CollectingDescriptor. \\ \end{tabular} \\ \hline Gyro measurement range & GR & \({}^{\circ}\)/s & \begin{tabular}{c} :Slope rdf:type owl:Class; \\ rdfs:subClassOf:SiteEnvironment. \\ \end{tabular} \\ \hline Rain rate & RR & \begin{tabular}{c} - \\ \end{tabular} & \begin{tabular}{c} :Rainfall rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Soil moisture & SM & \begin{tabular}{c} - \\ \end{tabular} & \begin{tabular}{c} :Soil Moisture rdf:type owl:Class; \\ rdfs:subClassOf:SiteEnvironment. \\ \end{tabular} \\ \hline Ultraviolet & UV & nm & \begin{tabular}{c} :Ultraviolet rdf:type owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Carbon monoxide & CO & ppm & \
\begin{tabular}{c} :Carbonmonoxide Concentration rdf:type \\ owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline Nitrogen dioxide & NO\({}_{2}\) & ppb &
\begin{tabular}{c} :Nitrogen dioxide Concentration rdf:type \\ owl:Class; \\ rdfs:subClassOf:ClimateOfTheSite. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mapping ontology classes to relevant sensors’ measurement types. Measurement values of mapped data types can be viewed using open-data platform and they will be integrated with hazelnut datasets. As shown in Figure 6, decomposed sensor data streams are mapped with the relevant hazelnut trait ontology classes by rule engine using mapping rules. One of the rule examples, which represents mapping the temperature measurement data type with: _Temperature_ class of hazelnut trait ontology, is illustrated in Table 6.  
It is apparent from this example that rules use URI reference to identify resources in datasets stored in RDF files. The base URI of hazelnut trait ontology is [[http://www.opendatainagiculture.com/](http://www.opendatainagiculture.com/)]([http://www.opendatainagiculture.com/](http://www.opendatainagiculture.com/))