# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
It is shown in Figure 14 that the SM-DTR model outperformed the SM-kNN and SM-SVR models in terms of precision, recall, F1-score, and accuracy. Both precision and recall values achieved by the SM-DTR model were 0.98. The accuracy of the SM-DTR model was 96%, compared to 93% and 92% for the SM-kNN and SM-SVR models, respectively. The F1-score for the SM-kNN and SM-SVR models were the same at 0.96 compared to 0.98 for the SM-DTR model. The ROC curves for the three variants of the proposed method were also drawn at threshold 0.80 as shown in Figure 15. It can be observed that the SM-DTR model outperformed the SM-kNN and SM-DTR models, achieving 89% of AUC ROC, thus indicating its superior capability and accuracy. A noteworthy finding is the improved performance of the SM-SVR model that achieved 81% of AUC ROC. The SM-kNN model, on the other hand, ranked third with 80% of AUC ROC with a slight difference from the SM-SVR model's performance. The results of the ROC curves in Figure 15 are in line with the precision and accuracy values presented in Table 7 and Figure 14. These results further validate the performance of the kNN, SVR, and DTR classifiers and their suitability for the ontology alignment task. Figure 16: Accuracy, precision, recall, and F1-score of SM-kNN, SM-SVR, and SM-DTR models at threshold 0.90. Figure 17: ROC curves of SM-kNN, SM-SVR, and SM-DTR models at threshold 0.90. **(a)** SM-kNN, **(b)** SM-SVR, **(c)** SM-DTR. It is shown in Figure 16 that the SM-SVR model underperformed relative to the other models, exhibiting a low accuracy of 32%, and zero precision, recall, and F1-score. Conversely, the SM-DTR model continued to demonstrate superior performance relative to the others. Notably, the F1-score of SM-DTR was higher than that of SM-kNN model, with a score of 0.91. Additionally, the SM-DTR model exhibited the best accuracy of 88%, while SM-kNN was slightly behind with an accuracy of 82%. The ROC curves of the SM-kNN, SM-SVR, and SM-DTR models at threshold 0.90 are presented in Figure 17. It is shown in Figure 17 that the SM-DTR model kept its superior performance compared to SM-kNN and SM-SVR with 84% predictions of true positives and true negatives. The performance of the SM-SVR classifier at threshold 0.90 was the worst compared to that at previous thresholds 0.70 and 0.80, while the SM-kNN performance was promising with a slight decrease from its performance at threshold 0.80. Overall, the results presented above portrayed the SM-DTR as the best model for hybridizing the fuzzy string-matching algorithms and the BERT deep learning model for improved ontology alignment. These results also demonstrated the potential of the kNN classifier as the second-best classifier for combining the strengths of fuzzy string-matching algorithms and the BERT deep learning model to achieve better ontology alignment performance. With regard to the SM-SVR model, the results indicated that it could be used with caution as it may display poor performance at certain threshold levels. ### Analysis of Error Rates of SM-kNN, SM-SVR, and SM-DTR Models

To further evaluate the performance of the three models, we calculated the Mean Squared Error (MSE) and the Root Mean Squared Error (RMSE) for each classifier. Table 9 presents the MSE and RMSE for each classifier. It is shown in Table 9 that the MSE and RMSE for the three models are very low. The best model was SM-DTR with MSE and RMSE of 0.003 and 0.050, respectively. The SM-SVR model recorded the highest MSE and RMSE of 0.008 and 0.091, respectively. These results are consistent with the above-mentioned findings regarding the performance of the classifiers in terms of precision, recall, F1-score, and accuracy. These results further confirm the SM-DTR as the best model among the three as it exhibited superior performance in terms of error rates and other key metrics. ### Analysis of Processing Time of the SM-kNN, SM-SVR, and SM-DTR Models

Table 10 shows the running time of SM-kNN, SM-SVR, and SM-DTR models for performing the 1400 alignments, in seconds.