# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
However, the SM-SVR model outperformed the others by predicting 1293 true positive cases, while the SM-DTR model detected the least number of true positives with 1269. Comparing the number of true positive cases predicted by the three models to those of fuzzy string-matching algorithms and BERT (Figures 3-5), one can see that the three hybrid models (SM-kNN, SM-SVR, and SM-DTR) of the proposed method based on kNN, SVR, and DTR classifiers outperformed the fuzzy string-matching algorithms and BERT. This indicates the effectiveness of the proposed method that harnesses the strengths of both the fuzzy string-matching algorithms and the BERT model for improved ontology alignment. Figure 6: Confusion matrix for SM-kNN, SM-SVR, and SM-DTR models at threshold 0.70. (**a**) SM-kNN, (**b**) SM-SVR, (**c**) SM-DTR. Figures 7 and 8a-c presents the confusion matrixes for the SM-kNN, SM-SVR, and SM-DTR models at threshold 0.80 and 0.90. At threshold 0.80, the SM-DTR model outperformed the SM-kNN and SM-SVR models, with a higher number of true positives of 1243 cases predicated, followed by the SM-kNN model with 1213 true positives, and SM-SVR with 1206 true positives. The SM-DTR model also achieved a better performance at threshold 0.90 compared to other models with 893 true positives, followed by the SM-kNN model with 847 true positives. The SM-SVR model achieved the worst performance at threshold 0.90 with 0 true positives. These results further indicate that the combination of the strengths of fuzzy string-matching algorithms



In the scatterplot diagram for the SM-SVR model in Figure 10 the majority of plots are clustered around the center-right of the diagram, with similarity scores ranging from 0.77 to 0.86. However, a few plots appear beyond 0.90, indicating performance close to that of the AML reference scores. Figure 11 displays the scatterplot diagram for the SM-DTR model. It appears that a large number of plots form a line with a 45-degree inclination angle, indicating that the majority of predictions made by the SM-DTR model are true positives. Furthermore, a significant number of plots are situated in the upper right corner of the diagram, demonstrating the ability of the SM-DTR model to accurately predict similarity scores with highest values between classes. Figure 10: Scatterplot diagram of similarity cores predicted by SM-SVR vs. AML reference scores. Figure 9: Scatterplot diagram of similarity cores predicted by SM-kNN vs. AML reference scores. #### 5.2.2 Performance of the SM-kNN, SM-SVR, and SM-DTR Models at Threshold 0.70

The performances of the SM-kNN, SM-SVR, and SM-DTR models were further discussed in terms of precision, recall, and F1-score, at thresholds 0.70, 0.80, and 0.90. The evaluations of the proposed hybrid method at threshold 0.70 are presented in Table 6 and Figure 12. Table 6 presents the precision, recall, F1-score, and accuracy values of the classifiers at a threshold of 0.70. It is shown that the SM-DTR model achieved the highest precision of 0.98, while the SM-SVR model had the lowest precision of 0.93. On the other hand, the SM-SVR model achieved the highest recall of 1.00, followed by the SM-kNN and SM-DTR models. However, the SM-DTR model outperformed the other methods with the best F1-score of 0.98, indicating the ability of the DTR classifier to strike a good balance between precision and recall. Moreover, the SM-DTR model had the highest accuracy of 97%, while the SM-kNN and SM-SVR models achieved the accuracies of 94% and 93%, respectively. These metrics are also depicted in Figure 12, which corroborates the results presented in Table 6, highlighting that the DTR is the most accurate classifier for the proposed method, achieving the best precision, F1-score, and accuracy. #### 5.2.3 Performance of the SM-kNN, SM-SVR, and SM-DTR Models at Threshold 0.80

The performance of the SM-kNN, SM-SVR, and SM-DTR models at threshold 0.80 is illustrated in Table 7 and Figures 14 and 15.