## 1 Introduction  
Despite significant advancements in diagnosis and prevention, many diseases continue to pose a serious threat to human health, killing millions annually. The World Health Organization (WHO) reports estimate that chronic diseases (non-communicable diseases, NCDs) are responsible for 71% of all deaths globally, or 41 million people each year [1]. The four main types of NCDs are cardiovascular diseases, cancers, respiratory diseases, and diabetes [1]. Asthma, a chronic lung condition, causes swollen and narrow airways, making breathing difficult. It affects approximately 339 million people globally, including over 3.8 million in Canada [2]. The two main types are allergic asthma, triggered by allergens and often starting early in life, and non-allergic asthma, which has no identifiable triggers and typically begins later [3]. Allergic asthma is more common and usually responds better to treatment, accounting for 75-80% of cases [4]. Knowledge representation refers to structuring information to ensure efficient storage and easy retrieval [5]. In health care, knowledge representation involves capturing medical knowledge in a way that computers can understand. This helps support doctors and nurses in making better decisions [6]. By representing medical knowledge in a format that computers can use, healthcare providers can improve diagnosis, treatment planning, and medication management. Knowledge representation models (KRMs) also help analyze patient data across groups, allowing healthcare providers to identify diseases more effectively [7]. The representation of the medical context requires a deep understanding of the healthcare domain and its various dimensions. Context can significantly enhance the representation of knowledge, particularly in the healthcare domain [8,9]. Consequently, medical data can encompass several parameters, and the acquisition process must facilitate data representation that enables understanding, standardization, and centralized management of all diseases. Data now plays a crucial role in decision-making across multiple fields. However, datasets in the medical domain are often complex and disorganized, making direct extraction of useful information difficult [10,11]. Asthma represents a chronic disease that can be investigated through a patient's signs, medical history, demographic information, and other parameters [12]. We distinguish many KRMs, such as frame, UML, and graphical representation. We focus on ontology models that facilitate the management of data by incorporating relevant domain concepts and their associated relations [9]. This shared knowledge system allows for a comprehensive representation of complex information, which is crucial in fields such as health care, where data interdependence is significant [9]. Ontologies help in structuring and managing healthcare data to offer tailored services [13]. Ontologies are used in 50% of applications, emphasizing their crucial role in organizing and representing data. The other 50% comprises alternative modelling approaches, demonstrating a balanced distribution among KRMs [14]. The development and integration of standard ontologies, such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), facilitate the exchange of information through medical information systems [15]. The use of standard ontologies ensures consistent and semantically interoperable data management across different healthcare systems [15]. The main contributions of this paper are:  
1. Designing a general contextual ontology model that includes the key knowledge domains related to chronic disease management. 2. Extending this general contextual ontology to a domain-specific model for asthma, highlighting the identification of classes, entities and their interrelations. 3. Supporting the evaluation process and enhancing the inference capabilities (reasoning) of this extended asthma ontology by extracting the rules using artificial intelligence (AI) algorithms. 4.