# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 2 Related Work  
In recent years, a considerable amount of research has been conducted on ontology alignment methods [13; 17; 33; 34; 35; 36; 33]. The purpose of ontology alignment is to investigate the differences and commonalities between ontologies, which were created by various organizations with distinct objectives and data sources. To aid in this process, various metrics have been introduced, including lexical (also called string-based, syntactic, content-based), structure-based, and semantic metrics. Other studies mentioned further metrics such as hierarchy metric and heuristics [15]. Lexical metrics focus on linguistic matching showing the degree of similarity between concepts' labels (strings). This metric is particularly useful when ontologies in the same or related domains describe similar knowledge with basic linguistic terms, since they are likely to use the same globally recognized terms within a given domain. However, lexical metrics alone are insufficient as they do not capture the semantic features of ontologies' elements. Furthermore, various challenging cases, such as synonyms, word omissions, abbreviations, misspellings, and other factors, prevent this metric from being considered for ontology alignment alone [37]. To address this limitation, semantic metrics consider the underlying concepts' semantics by examining their axioms in both their direct and logical forms [15]. Structure-based metrics, on the other hand, focus on the degree of structural similarity between aligned ontologies by identifying neighboring classes, associated properties, and other structural characteristics [15]. Finally, hierarchy metrics examine the ontology's design hierarchy, such as the relationships between classes and the hierarchy of classes (super- and sub-classes). Hence, both the structure-based and hierarchy metrics are elements of the semantic metrics of ontologies [36; 38]. Recent studies have introduced methods for ontology matching that incorporate more than one metric at a time [13; 17; 30; 34; 39; 40]. In a study [13], the authors proposed a novel approach that combines word embedding with a random forest classifier to identify semantic similarities among various concepts. They evaluated their approach using precision, recall, and F1-score, demonstrating promising results compared to alignment systems in OAEI 2016. Their results show that they achieved a precision of 0.90 and a recall of 0.71, which are lower than the best precision of 0.98 and a recall of 1.0 achieved in this study. Additionally, this study used other metrics such as the running time, Mean Squared Error,and Root Mean Squared Error, to evaluated the performance of the proposed ontology alignment models, while the researchers in [13] did not. A similar study by [PERSON] [29] proposed an approach that performs ontology alignment by combining the lexical and semantic metrics of ontologies using machine learning techniques. Three classifiers were used in the experiment, namely Naive Bayesian, Gradient Tree Boosting, and Logistic Regression. Furthermore, the EditDistance and WordNet were used as baseline for lexical and semantic similarity discovery, respectively. The results showed that the classifiers achieved high precision and recall, and the Gradient Tree Boosting classifier outperformed other classifiers with 55.01% in terms of F-measure. The difference between [PERSON] [24] and this study is that we used a different approach that combines fuzzy string-matching algorithms, the BERT deep learning model, and machine learning regression models to perform both lexical and semantic alignments of ontologies. Another difference between our work and [PERSON]'s study is that we evaluated the performance of the lexical alignment by different fuzzy string-matching algorithms and the performance of the semantic alignment with the BERT deep learning model, then we combined both performances with machine learning regression models and compared the final results of the method to that of recent state-of-the-art alignment systems in the OAEI 2022 challenge.