## 4 Case Study: Searching Physical Models in MATLAB Simulink
#### Analysis of Results  
The results presented in this subsection are based on the levels of \"goodness\" (as outlined in Table 2) established in [36], a metric framework that has found successful application in similar retrieval works such as those in [25,26]. Our results show that, according to the metrics and the extracted performance metrics (see Table 3), all queries except four obtained the maximum precision value when retrieving physical models, which can be classified as excellent in terms of precision. Only one query performed below the excellent level for precision, which was because the implemented search algorithm did not take into account the semantic information about the different connection types between \begin{table}
\begin{tabular}{c l} \hline \hline \(\mathbf{Q}\) & \multicolumn{1}{c}{**Query Description**} \\ \hline \(\mathrm{Q}_{1}\) & Circuits \\ \(\mathrm{Q}_{2}\) & Models with a DC power supply of 200 V \\ \(\mathrm{Q}_{3}\) & Models with hydraulic transfer functions \\ \(\mathrm{Q}_{4}\) & Models with an integrator \\ \(\mathrm{Q}_{5}\) & Electrical models using diodes \\ \(\mathrm{Q}_{6}\) & Sum operations with integrators \\ \(\mathrm{Q}_{7}\) & Resistor connected in series with a resistor \\ \(\mathrm{Q}_{8}\) & Models with memory \\ \(\mathrm{Q}_{9}\) & Circuits with supercapacitors \\ \(\mathrm{Q}_{10}\) & Resistors connected in parallel with diodes \\ \(\mathrm{Q}_{11}\) & Models with transfer functions \\ \(\mathrm{Q}_{12}\) & Models with switches \\ \(\mathrm{Q}_{13}\) & Models with voltages of at least 100 V \\ \(\mathrm{Q}_{14}\) & Models with a 3-phase harmonic filter \\ \(\mathrm{Q}_{15}\) & Models with SPICE resistors \\ \(\mathrm{Q}_{16}\) & Physical models with torque \\ \(\mathrm{Q}_{17}\) & Models with logical operations \\ \(\mathrm{Q}_{18}\) & A transistor connected in parallel with a resistor \\ \(\mathrm{Q}_{19}\) & Circuits with logical operations \\ \(\mathrm{Q}_{20}\) & Battery of 12 V \\ \(\mathrm{Q}_{21}\) & Models with sum operations \\ \(\mathrm{Q}_{22}\) & Bang–bang controller \\ \(\mathrm{Q}_{23}\) & AC voltage source of 100 V \\ \(\mathrm{Q}_{24}\) & Models with functions \\ \(\mathrm{Q}_{25}\) & Physical models with switches \\ \(\mathrm{Q}_{26}\) & Models with a signal input \\ \(\mathrm{Q}_{27}\) & A transistor in series with a resistor \\ \(\mathrm{Q}_{28}\) & Aerospace models \\ \(\mathrm{Q}_{29}\) & Capacitor connected with a voltage of 10 V \\ \(\mathrm{Q}_{30}\) & Models with thermocouples \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of queries executed to retrieve similar models. electronic components (parallel or series). In terms of recall, 58% of all executed queries achieved an excellent value, and all queries had values higher than what is considered good. When analysing the overall average metrics, it is important to highlight that the recall values are lower than the precision values. These findings indicate that while relevant models for a query are retrieved, there is some retrieval of irrelevant ones as well, which adversely impacts the value of the recall metric. This issue could be addressed by fine-tuning the weights and adjusting the penalty function to minimise the retrieval of irrelevant models and enhance overall performance. The semantic search process conducted in the case study takes into account the content of the model alongside the generated description text. This contrasts with the approach presented in previous studies [25], where search was based on model metadata and a limited vocabulary was represented in an ontology. The results of our case study show that the proposed approach represents a promising alternative that can assist engineers in reusing the content of physical models. By processing the distance between the terms embedded in the models using a trained model of word