# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 1 Introduction  
Ontologies are the fundamental building blocks of the semantic web, an emerging field of Artificial Intelligence (AI) that seeks to integrate data and information in a formalized manner [1], allowing for automated understanding and discovery of connections between them. An ontology is an explicit specification of a shared conceptualization [2], serving as a container for knowledge in a specific domain where the relationships between concepts of the ontology are explicitly defined. By providing a standard vocabulary and formal structure for data representation, ontologies enable the reuse and sharing of knowledge across different applications and domains. As such, ontologies play a critical role in advancing the state-of-the-art in real-world applications. Many ontologies have been developed in diverse domains, including disease [3], agriculture and the Internet of Things [4], smart home design [5], climate change [6], and more [7; 8; 9; 10]; these existing ontologies constitute a wealth of valuable assets available for reuse. The practice of ontology reuse involves leveraging existing ontologies to construct new ones within the same or related domains. To this end, researchers have defineda common framework for ontology reuse that involves ontology selection and ranking, followed by ontology alignment, integration, and/or merging, and finally, evaluation of the new constructed ontology [11; 12]. Given the wide array of ontologies available in various domains of knowledge with heterogeneous structures and vocabularies, reusing them is a daunting task that requires meticulous and precise comparisons to identify overlapping and differences between them. This is where ontology alignment, also known as ontology matching, plays a crucial role. However, the challenges inherent in the reuse process are also reflected in ontology alignment [13; 14; 15; 16], given that ontologies are developed independently by different knowledge engineers, each with their unique perspectives. Therefore, a holistic approach must be taken when aligning ontologies, accounting for variations in vocabulary, concept semantics, and structures. Despite numerous efforts to develop effective ontology alignment methods, such as in the Ontology Alignment Evaluation Initiative (OAEI), challenges persist, making it a fascinating and active area of research. A range of metrics have been proposed for ontology alignment, including lexical, structure-based, and semantic metrics [15; 17]. Fuzzy string-matching algorithms and other string matching techniques have been shown to be effective for lexical alignment in several studies [18; 19]. However, these methods may not be sufficient on their own, as they do not consider the semantic features of ontologies, especially when concepts are linguistically different but represent the same underlying knowledge, such as acronyms and synonyms. Additionally, some machine learning-based methods used for ontology selection and alignment such as Word2 Vec and Skip-gram are not able to recognize previously unseen vocabulary [20; 21]. Some studies have proposed structure-based methods for ontology alignment [22; 23; 24], but these are limited by the different goals and purposes of the design of the target ontologies [23]. Many studies have also made use of external information (metadata) of the ontology in their methods and graphs for ontology matching to overcome the issue of the complexity of semantic relationships between concepts [25; 26; 27; 28]. Other authors have proposed metrics that combine both semantic and syntactic measures [13; 29; 30]. However, they have limited performance compared to existing state-of-the-art alignment systems such as the AML [31] and LogMap [32], which participated in the OAEI 2022 challenge. To address the shortcomings of existing ontology alignment techniques, this study proposed a novel method that hybridizes fuzzy string-matching algorithms with the BERT deep learning model to achieve improved ontology alignment results.