# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
Table 10 shows that the SM-DTR model achieved the lowest processing time among the three classifiers, aligning the entire dataset in the least amount of time. Conversely, the SM-SVR model obtained the highest processing time of 0.0147 s. The SM-kNN model recorded a processing time that felt between the other two models. Overall, the results indicate that all three models achieved relatively low processing times. However, the SM-DTR model emerged as the most computationally efficient classifier for the proposed \begin{table}
\begin{tabular}{c c c} \hline \hline
**SM-kNN** & **SM-DTR** & **SM-SVR** \\ \hline
0.0045 & 0.0011 & 0.0147 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Running time of SM-kNN, SM-SVR, and SM-DTR models in seconds. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SM-kNN** & **SM-DTR** & **SM-SVR** \\ \hline MSE & 0.003 & 0.003 & 0.008 \\ RMSE & 0.054 & 0.050 & 0.091 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MSE and RMSE for the SM-kNN, SM-SVR, and SM-DTR models. method in this study, as it has performed the alignment of the dataset in the least amount of time while also exhibiting superior performance in terms of accuracy and error rates. It was also found that the processing times of all classifiers are relatively low than those of the state-of-the-art systems in OAEI-2022; this is going to be discussed further in the next section. Therefore, in terms of computational efficiency, specifically the running time, our proposed method performs better than some state-of-the-art alignment systems in OAEI 2022. The comparison of our proposed method with the state-of-the-art alignment systems in the OAEI 2022 is presented next. ## 6 Comparison of the Proposed Method with State-of-the-Art Alignment Systems

In this section, we provide a comparison between our proposed alignment method and state-of-the-art alignment systems that participated in the OAEI 2022. We compare our results to the alignment systems in the Anatomy track of the OAEI 2022 challenge, which involved aligning the _mouse_ and _human_ ontologies, the same used in the experiments in this study to enable an accurate comparison. The web link for accessing information on the Anatomy Track of the OAEI 2022 challenge is given in the Supplementary Materials section of this article. The performances of the participating alignment systems at the OAEI 2022 challenge are presented in Table 11. Let us recall that the performance of our proposed model, for the metrics reported in Table 11, are provided in Tables 6-8 at threshold 0.70, 0.80, and 0.90, respectively, and could not be repeated in Table 11. Therefore, the discussions in this section compare the results in Table 11 to those of this study in Tables 6-8. Table 11 provides the performance of the ontology alignment systems that participated in the OAEI 2022 challenge. The table presents the runtime, precision, F-measure, and recall for each system. It is important to note that the alignment process in OAEI 2022 does not solely rely on lexical alignment but also considers semantic aspects, such as structures, siblings, super- and sub-classes, when generating reference similarity scores between classes. Our proposed method utilized a hybrid approach of the BERT model and fuzzy string-matching algorithms with machine learning regression models to achieve better alignment performance. By taking this approach, we were able to reduce the time for the alignment significantly as evidenced by the low running times of our models in Table 10 compared to the second column of Table 11 for the various state-of-the-art alignment systems. The classifiers used in our approach exhibited faster processing times, demonstrating the efficiency and effectiveness of our method in aligning ontologies. This is a significant advantage, as reducing processing time is crucial in this era of big data where ontologies may include thousands of concepts to process.