# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 1 Introduction
Unlike existing fuzzy string-matching algorithms, the proposed method incorporates both the lexical/syntactic and semantic features of ontologies in the alignment process. This was achieved by combining the similarity scores obtained from four fuzzy string-matching algorithms, including the Longest Common Subsequence (LCS), Levenshtein, Jaccard, and Jaro-Winkler, and the similarity scores of the BERT deep learning model with three machine learning regression classifiers including kNN, SVR, and DTR. The aim of the method was to combine the lexical contents of ontologies and the context and semantic properties of ontologies, processed by the fuzzy string-matching algorithms and the BERT model, respectively, for comprehensive and improved ontology alignment performance based on machine learning. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three hybrid models, namely, SM-kNN, SM-SVR, and SM-DTR. The experiments were conducted on a dataset obtained from the anatomy track in the OAEI 2022 challenge. The SM-kNN, SM-SVR, and SM-DTR models were trained using the similarity scores obtained by the fuzzy string-matching algorithms and the BERT model for each pair of classes in the input ontologies, as well as the similarity scores obtained by the AML reference alignment system for the same input ontologies. The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97,and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 ontology alignment challenge, indicating the superior capability of the proposed method. The main contributions of this study are three-fold:  
* The development of a novel method that considers both the lexical and semantic features of ontologies in the alignment process to address the limitations of fuzzy string-matching algorithms,
* The use of deep learning and machine learning regression models to improve ontology alignment results, thereby demonstrating the potential of machine learning in enhancing performance in the field of ontology alignment, and
* The accomplishment of a thorough and detailed performance analysis of the proposed method against state-of-the-art alignment systems in the OAEI 2022 ontology alignment challenge. The remaining of this paper is structured as follows: In Section 2, we review the relevant literature and discuss previous studies related to ontology alignment. Section 3 outlines the materials and methods used in this study, including algorithms, machine learning techniques employed, and metrics used for the evaluation of the proposed method. In Section 4, we provide a detailed description of our proposed method, which combines fuzzy string-matching algorithms and deep learning and machine learning techniques. Section 5 presents and discusses the experimental results obtained. Section 6 compares our proposed method with state-of-the-art alignment systems. Finally, in Section 7, we draw conclusions from our findings and suggest potential avenues for future research.