# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
In our proposed method, the SM-DTR model demonstrated superior precision performance of 0.98 at both thresholds 0.70 and 0.80 (Tables 6 and 7). Among the OAEI 2022 matchers, only StringEquiv and ALIN matchers achieved the same precision score. Our SM-kNN and SM-SVR models also performed well in our proposed method, achieving their best precision scores of 0.96. In comparison to the OAEI 2022 matchers in Table 11, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mather** & **Runtime** & **Precision** & **F-Measure** & **Recall** \\ \hline ALIN & 374 & 0.984 & 0.852 & 0.752 \\ ATMatcher & 156 & 0.978 & 0.794 & 0.669 \\ LogMap & 9 & 0.917 & 0.881 & 0.848 \\ LogMapBio & 1183 & 0.873 & 0.895 & 0.919 \\ LogMapLite & 3 & 0.962 & 0.828 & 0.728 \\ LSMatch & 20 & 0.952 & 0.761 & 0.634 \\ Matcha & 37 & 0.951 & 0.941 & 0.93 \\ ALIon & 26134 & 0.364 & 0.407 & 0.46 \\ SEBMatcher & 35602 & 0.945 & 0.908 & 0.874 \\ AMD & 160 & 0.953 & 0.88 & 0.817 \\ StringEquiv & - & 0.997 & 0.766 & 0.622 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for OAEI 2022—Anatomy track [59]. our method outperformed the majority, with SM-kNN and SM-SVR coming in fourth place. In terms of recall, our proposed method outperformed the majority of the OAEI 2022 matchers. Specifically, SM-SVR model achieved a perfect recall score of 1.0 at threshold 0.70 (Table 6), surpassing all other matchers in the OAEI 2022 challenge. Our SM-kNN model followed closely with a recall score of 0.99 at the same threshold, which also outperformed the OAEI 2022 matchers. Meanwhile, our SM-DTR model achieved a recall score of 0.98 at thresholds 0.70 and 0.80 (Tables 6 and 7), which is still superior to most of the OAEI 2022 matchers in Table 11. Notably, even at the most stringent threshold of 0.90, our SM-DTR still outperformed all OAEI 2022 matchers with a record performance of 0.94 in terms of recall. Our SM-kNN model's recall of 0.89 (Table 8) placed it at the third position behind the Matcha and LogMapBio systems (Table 11), which achieved a recall of 0.93 and 0.919, respectively. Although our SM-SVR model did not perform as well at threshold 0.90, it still demonstrated impressive performance at thresholds 0.80 and 0.70 (Tables 6 and 7) compared to the alignment systems in Table 11. Regarding the F-measure, our proposed method achieved superior results compared to the OAEI 2022 systems. Our SM-DTR model recorded the best F-measure score of 0.98 at both thresholds 0.70 and 0.80 (Tables 6 and 7), outperforming the best OAEI 2022 system, namely, Matcha, which achieved an F-measure score of 0.94 (Table 11). These results demonstrate the effectiveness of our proposed method in accurately aligning ontologies, outperforming the state-of-the-art alignment systems in terms of recall and F-measure. In addition to the above-mentioned metrics used in the OAEI 2022 challenge, our proposed method was also evaluated based on the accuracy (Tables 6-8) as an important measure of alignment systems' performance. Additionally, error rates (MSE and RMSE) were also measured for all three proposed models. These additional metrics provides a more comprehensive evaluation of alignment systems' performance compared to the OAEI 2022 challenge. Based on the discussions above, this study has proposed a novel method that successfully hybridized fuzzy string-matching algorithms and BERT deep learning model with machine learning regression classifiers for improved ontology alignment performances and the proposed method has outperformed the state-of-the-art alignment systems. In particular, our SM-DTR model based on DTR achieved the best overall performance at different thresholds. Our SM-kNN model came in second place, also demonstrating strong performance at different thresholds, whereas our SM-SVR model struggled at a stricter threshold like 0.90 but performed very well at lower thresholds. Overall, our method offers significant improvements in ontology alignment performance compared to existing systems.