## 3 Results
### Knowledge Extraction: SPARQL Queries
#### 3.2.1 Example 1: Retrieve the Brinell Hardness of All Tested Specimens  
Appendix A.1 outlines the SPARQL query algorithm designed to extract the average Brinell hardness values and their corresponding units for all metallographic specimens modeled as input for the Brinell indentation process. It achieves this by matching specific patterns in the WHERE clause and binding the relevant values to the variables defined in the SELECT statement. For a more intuitive interpretation of the query structure, a graphical representation has been created using the'sparqlgraphviz' package for Python [41], as shown in Figure 11. This visualization illustrates the pattern followed by the SPARQL query to identify all instances of the class MetallographicSpecimen, which are modeled as input for an instance of the class BrinellIndentation (process_node). Each output instance of the BrinellIndentation process is represented by an instance of the ProcessDataSet class, linked to a specific average Brinell hardness value and its corresponding unit. Additionally, every instance of the MetallographicSpecimen class is connected to an instance of the ObjectID class, which is associated with a specific specimen name through the data property hasStringLiteral.  
The information retrieved by the SPARQL query is presented in Table 2, displaying only the first five matches for clarity.  
2.2 Example 2: Retrieve the Process Control Parameters Corresponding to One Specific Object Identifier  
This example demonstrates how data structures based on the generic Process Graph template enable the search for process control parameters given a specimen identifier or label. In the present case, the user seeks information regarding a specific specimen, referred to as specimen_of_interest and labeled with the object identifier ARI_A120_S3_Z. The user wants to know:  
* Which process(es) utilized this specimen, i.e., the associated process_name;
* Which set of parameter quantities did control the process(es) for which the specimen_of_interest was used for;
* The values and corresponding units of the set of parameters controlling the output of the process(es).  
This knowledge can be extracted from the RDF graph by running the SELECT query presented in Appendix A.2. A graphical representation of this query is depicted in Figure 12. It shows how first the specimen with given object identifier ARI_A120_S3_Z is located, then follows the pattern given by the object property isInputFor and finds the process for which this specimen was used. Finally, through the object-property relationship hasControlInfo linking the process_node and the process_parameter_set_node instances, finds the connection to the process_parameter_node instance(s) and their attached data property value(s).  
The query result returning the process control parameters, values and units, as well as the process they belong to, for the specimen with given identifier ARI_A120_S3_Z are summarized in Table 3. This result clarifies that the investigated specimen was employed in a quasi-static tensile test, controlled by the specified values for crosshead separation rate and original gauge length, along with their respective units.  
Figure 12: Visualization of the SPARQL query algorithm retrieving the process control parameters corresponding to one specific object identifier.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process\_Name** & **Parameter\_Name** & **Parameter\_Value** & **Unit\_Symbol** \\ \hline QuasiStaticTensileTest\_1 & CrossheadSeparationRate\_1 & \(3.0\times 10^{-2}\) & mm/s \\ QuasiStaticTensileTest\_1 & OriginalGaugeLength\_1 & \(9.79\times 10^{7}\) & μm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of the control process parameters query for the specimen with object identifier ARI_A120_S3_Z.