## 1 Introduction  
In recent times, the explosion of digital data [1], tight coupling with the convenience of digitization, diverse data representations and descriptions, and varying personal preferences have led large enterprises to store vast amounts of data in multiple formats. These formats range from structured relational databases to unstructured flat files, reflecting the increasing complexity of data management and highlighting the growing complexity of data storage and integration [2, 3]. The influx of global data volume is predicted to touch approximately 163 zettabytes by 2025 [4]. Large enterprises in different sectors, such as healthcare, will generate almost half of those data. According to healthcare sector observations, most _healthcare information systems_ (HISs) are patient-centric, granting patients certain privileges to access electronic health record systems, leading to the collection of vast amounts of clinical data online. However, developing integrated HIS is challenging because of _Data Integration and Semantic Interoperability_ (DI&SI) issues [4; 5]. DI&SI challenges create a severe bottleneck for seamless data exchange and access to other applications within or across healthcare organizations [4]. In this context, the lack of DI&SI arises from variations in data structures, limited standardization, and diverse formats, hindering data access and resulting in inefficiencies, increased costs, poor quality, scalability and performance bottlenecks, and semantic alignment challenges, as well as a lack of comprehensive virtualization frameworks constructing monolithic ontological artifacts that integrate diverse healthcare data models cohesively [4; 5; 6]. In a distributed data environment, the interconnected legacy health systems demand seamless integration across both internal and external distributed rational data sources and disparate data models that are distinct in nature, purpose, and contextual relevance [4]. Traditional approaches [7] involve transforming data for storage and distribution within a unified repository through physical consolidation, such as data warehousing (DW) using data integration tools [8]. Alternatively, disparate data can be logically merged through an integration layer [4; 9]. Vendors have expanded their integration tools to enable service-based architecture, data fabrics, and middleware like GraphQL [10], enabled by graph-based solutions. [PERSON] [8] identified data fabric and _Knowledge Graphs_ (KGs) as key technological trends in data management frameworks. These frameworks enable a standardized query approach, establish relationships across diverse data sources and models, and introduce a shared vocabulary for data interchange and integration, such as _the Resource Description Framework_ (RDF) [11]. In desktop research, data practitioners advocate for semantic technologies, particularly ontologies [12] and KGs [13], as solutions to data bottlenecks [4]. Ontologies concisely represent a structured representation of domain knowledge through a shared conceptualization with formal and explicit definitions [14]. Observations show that heterogeneous data sources can be effectively described and harmonized using ontologies, following the _Ontology-based Data Integration_ (OBDI) architecture across various domains [4; 15]. Therefore, [PERSON] et al. [16] published a study on implementing _Federated Ontology_ (FO) to integrate disparate data models and map diverse relational data sources. Likewise, semantic-driven ontology generation aims to map a domain-centric ontology with existing databases. [PERSON] and [PERSON] [7] proposed a semi-automated database-ontology mapping approach extended from [PERSON] et al. [4; 16]. A limitation of OBDI is the need to create a local ontology for each new data source to connect it to the global ontology, a process that is often complex, time-consuming, and costly [4; 5]. OBDI is associated with _Ontology-based Data Access_ (OBDA) [15].