# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 2 Related Work
This approach allowed us to identify the strengths and weaknesses of each alignment metric and to determine which fuzzy string-matching algorithms may work best in combination with machine learning to improve ontology alignment results. In [30], the authors proposed an approach for ontology alignment that used a Convolutional Neural Network (CNN) to perform both lexical and structure alignments. The results showed that the proposed approach outperformed many alignment systems in the OAEI. However, the study performed lexical and structure alignments. The structures of ontologies may differ according to the objectives of their design. The variation of the ontologies' structures may cause the alignment algorithm to obtain unreliable alignment results [38]. Therefore, although the method in [30] showed promising results, it is important to combine both the lexical and semantic features of ontologies as it is done in this study to achieve more comprehensive and reliable alignment results. A group of studies harnessed BERT for ontology alignment. [PERSON] et al. [40] proposed an ontology matching system called BERTMap. Their system predicts semantic mapping by obtaining the contextual embedding from a text extracted from the ontologies, then it utilizes ontologies' structures and logic to refine the overall alignment. In [41], the authors used BERT to align two occupation ontologies. The authors arrived at the conclusion that BERT can contribute to ontology alignment. By combining manual and automatic alignment techniques through hybridization, it is possible to enhance coverage and eliminate errors. However, the use of manual alignment is unsuitable in real-world applications where the ontologies being matched may have thousands of concepts and axioms. Furthermore, manual alignment may be costly in terms of time and efforts required to perform the alignment. Therefore, in this study, BERT is combined with fuzzy string-matching algorithms in a fully automated way for ontology alignment. In the study [42], the authors utilized the BERTMap system introduced by [PERSON] et al. [40] to perform the alignment of biomedical ontologies and concluded that BERTMap is convenient for real-world applications. In another study by [PERSON] et al. [43], the authors study the BERT biomedical variants to see whether they outperform the Siamese Network and original BioBERT. Their results showed that the Siamese Network and BioBERT largely outperformed other variants of BERT biomedical. The authors emphasized the efficacy of BERT in ontology alignment since it has high capability to understand the context and semantics of the matched labels. This capability of BERT is what motivated its hybridization with fuzzy string-matching algorithms in this study. Many previous studies on ontology alignment have neglected to evaluate their proposed methods in terms of the running time, which is a crucial performance metric required in the OAEI [13; 29; 30]. In contrast, this study conducted a comprehensive evaluation of the proposed method including the assessment of its processing time, in addition to the others evaluation metrics such as precision, recall, and F1-score, to provide a more complete picture of the effectiveness and performance of the proposed ontology alignment approach.