# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 4 Proposed Method
## 7 Conclusions and Future Work

In this study, we examined the benefits of fuzzy string-matching algorithms in the ontology alignment process and demonstrated how their effectiveness can be enhanced by incorporating semantic information using BERT. We evaluated the performance of four fuzzy string-matching algorithms on their own and found that they perform less accurately at higher thresholds. However, our proposed method that combined similarity scores obtained from fuzzy string-matching algorithms and BERT achieved very promising results, outperforming fuzzy string-matching algorithms and some of the state-of-the-art alignment systems in the OAEI 2022 ontology alignment challenge. To evaluate the effectiveness of the proposed method, we conducted experiments at three different thresholds and compared the performance of three hybrid models, namely, SM-kNN, SM-SVR, and SM-DTR. Various metrics including precision, recall, F1-score, Mean Square Error (MSE), and Root Mean Square Error (RMSE), and running time were used to evaluate the performance of the hybrid models. The experimental results showed that SM-DTR was the most effective model for ontology alignment that considers both lexical and semantic characteristics of ontologies. With best-of precision, recall, and F1-scores of 0.98, SM-DTR outperformed the other models, followed by SM-kNN, which achieved a best F1-score of 0.97. Moreover, SM-DTR showed the highest accuracy of 96% compared to the other models. The results of the proposed method can be used in many applications. One application is the integration and reuse of ontologies into custom software applications. Another possible application includes facilitating ontologies merging in which alignment process is crucial to build upon. In terms of future work, several avenues can be explored to enhance the versatility and adaptability of our approach. One direction involves investigating the integration of our proposed model with complementary techniques, which would offer increased flexibility to support languages other than English. Additionally, leveraging the fine-tuning capabilities of BERT, a re-trained model tailored for specific purposes, such as aligning individuals or logical axioms, holds promise for custom applications. By pursuing these potential research directions, our model can be further optimized and refined to address a wider array of language-related challenges and requirements. The following supporting information can be downloaded at: [[http://oaei.ontologymatching.org/2022/results/anatomy/index.html](http://oaei.ontologymatching.org/2022/results/anatomy/index.html)]([http://oaei.ontologymatching.org/2022/results/anatomy/index.html](http://oaei.ontologymatching.org/2022/results/anatomy/index.html)) (accessed on 1 May 2023). Conceptualization, M.S.M.R. and J.V.F.-D.; methodology, M.S.M.R. and J.V.F.-D.; software, M.S.M.R. ; validation M.S.M.R. and J.V.F.-D.; formal analysis, M.S.M.R. and J.V.F.-D.; investigation, M.S.M.R. and J.V.F.-D.; resources, M.S.M.R. ; data curation, M.S.M.R. and J.V.F.-D.; writing--original draft preparation, M.S.M.R. ; writing--review and editing, M.S.M.R. and J.V.F.-D.; visualization, M.S.M.R. and J.V.F.-D.; supervision, J.V.F.-D.; project administration, M.S.M.R. and J.V.F.-D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Dataset, Results and source codes are available online at: [[https://drive.google.com/drive/folders/1u0](https://drive.google.com/drive/folders/1u0) ayQzFjnUkbqQnwwTdlAsvRhA9_golfF?usp=sharing]([https://drive.google.com/drive/folders/1u0](https://drive.google.com/drive/folders/1u0) ayQzFjnUkbqQnwwTdlAsvRhA9_golfF?usp=sharing) (accessed on 1 May 2023). The authors declare no conflict of interest. ## References

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