## 2 State-of-the-Art  
An ontology, as defined by [PERSON], is an explicit specification of a conceptualization. With the increasing volume, variety, and complexity of data, ontologies have become a key area of research. Ontology is one of the most comprehensive semantic frameworks for enabling knowledge representation, integration, and reasoning [15]. Initially, ontology was defined as an \"explicit specification of a conceptualization\" [16]. Later, [PERSON] described it as a \"formal specification of a shared conceptualization\" [17]. [PERSON] [18], combining these perspectives, defined ontology as a \"formal, explicit specification of a shared conceptualization\" [19]. A conceptualization refers to an abstract model that represents the real world through objects, concepts, entities, and their relationships within a specific domain. \"Explicit\" ensures that all concepts and constraints are clearly defined to avoid misinterpretation [19]. Semantic formalization is often used to interpret complex information, making it meaningful and accessible to machines [20]. This means that queries can be made based on the purpose of the data.  
In health care, ontologies have been applied in areas such as cancer treatment planning [21], EHR interoperability [22], rare disease data integration [23], and diabetes management [24]. They have also advanced drug discovery [25] and genomic-based precision medicine [26]. Many uses have arisen for ontology in health care during the last decade, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Ontology Application** & **Reference** \\ \hline
2012 & Chronically ill patients under treatment & [27] \\ \hline
2013 & Cancer diagnosis & [21] \\ \hline
2014 & Brain neoplasm disease & [28] \\ \hline
2015 & Semantic ontology for EHR & [22] \\ \hline
2015 & Rare diseases & [23] \\ \hline
2016 & Diabetes management & [24] \\ \hline
2017 & Health system ontology & [29] \\ \hline
2017 & Drug discovery & [25] \\ \hline
2018 & Genetic-based personalized health care & [26] \\ \hline
2018 & Chronic obstructive pulmonary disease & [11] \\ \hline
2019 & Mental health and therapy planning & [30] \\ \hline
2020 & COVID-19 pandemic response & [31] \\ \hline
2020 & Complex healthcare data structures & [32] \\ \hline
2021 & Health internet-of-things integration & [33] \\ \hline
2022 & Healthcare frameworks & [34] \\ \hline
2022 & COVID-19 and safety perspectives & [35] \\ \hline
2023 & Healthcare data privacy and epidemiology & [36] \\ \hline
2024 & Patient-centric care & [37] \\ \hline
2024 & Data preparation in health care & [38] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ontology uses in health care over the last decade.  
These ontologies are designed for specific diseases [39]. However, existing ontologies are often limited in scope, designed for specific diseases or use cases, and fail to address the diversity of healthcare data for chronic diseases within a unified framework [40; 41]. Moreover, most ontologies have not respected international medical terminologies; this limits the reuse and sharing of knowledge and still requires human intervention for tasks such as triggering applications and notifications [42; 43]. This article highlights the need for a general contextual ontology capable of representing diverse healthcare data, extending it to chronic diseases, and reusing it for different case-study diseases.