# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
Among the fuzzy string-matching algorithms, Jaro-Winkler had the highest accuracy score of 36.7%, while Jaccard achieved the lowest accuracy of 3.07%. The low accuracies indicate that fuzzy string-matching algorithms are not reliable at higher thresholds. On the other hand, BERT was generally stable and reliable despite the decrease in its accuracy to 73.8% at threshold 0.90 compared to 82.2% and 89.6% at thresholds 0.80 and 0.70, respectively. This demonstrates that BERT outperforms fuzzy string-matching algorithms at different threshold due to its ability to handle the semantic features of the input ontologies. Therefore, it can be concluded that while fuzzy string-matching algorithms can be useful for lexical alignment at low thresholds, they are not reliable for

Figure 5: Confusion matrix for fuzzy string-matching algorithms and BERT at threshold 0.90. (**a**) Levenshtein, (**b**) [PERSON], (**c**) [PERSON], (**d**) [PERSON], (**e**) [PERSON]. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Variable Name**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**BERT**} \\ \hline Precision & 0.872340426 & 0.88372093 & 1 & 0.769911504 & 0.832 \\ Recall & 0.043248945 & 0.040084388 & 0.030714286 & 0.091772152 & 0.76793 \\ F1-score & 0.08241206 & 0.076690212 & 0.05959806 & 0.16399623 & 0.79868349 \\ Accuracy & 34.7857143\% & 34.6428571\% & 3.0714286\% & 36.6428571\% & 73.7857143\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of fuzzy string-matching algorithms and BERT at threshold 0.90.

alignment. BERT, on the other hand, has proven to be a more powerful tool for ontology alignment. The following subsection presents the results of our proposed method for hybridizing the similarity scores of fuzzy string-matching algorithms and the BERT deep learning model by the kNN, SVR, and DTR classifiers for improved ontology alignment. As indicated earlier, the use of the three classifiers to hybridize the fuzzy string-matching algorithms and the BERT model in the proposed method resulted into three hybrid models, namely, SM-kNN, SM-SVR, and SM-DTR. Therefore, here and after, the performance of the proposed method (Figure 2) is discussed in terms of its three hybrid models, SM-kNN, SM-SVR, and SM-DTR. ### Performance of SM-kNN, SM-SVR, and SM-DTR Models

Let us recall that the aim of this study was to hybridize/combine the strengths of fuzzy string-matching algorithms and the BERT deep learning model to develop a method that considers both the lexical and semantic features of ontologies in the alignment process. By combining the strengths of both fuzzy string-matching algorithms and BERT, we sought to improve the accuracy and comprehensiveness of the alignment process. To this end, the similarity scores of the fuzzy string-matching algorithms and the BERT model (see part in Table 2) were further combined with three machine learning regression classifiers, namely, kNN, SVR, and DTR. To evaluate the performance of our proposed method, its three hybrid models, named SM-kNN, SM-SVR, and SM-DTR, were trained using the similarity scores obtained by the fuzzy string-matching algorithms and BERT for each pair of classes in the dataset, as well as the reference similarity scores obtained by the AML system. The following subsections discuss the performance of the three hybrid models (SM-kNN, SM-SVR, and SM-DTR) of the proposed method, in terms of precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. #### 5.2.1 Confusion Matrices and Visual Presentation of the Proposed Model's Performance

Figures 6-8 present the confusion matrices for the SM-kNN, SM-SVR, and SM-DTR models at thresholds 0.70, 0.80, and 0.90, respectively. It can be seen in Figure 6 that the three models performed almost the same in terms of the number of true positive cases they predicted.