## 3 Methodology for Designing General Contextual Chronic Disease Ontology
### Extending the General Contextual Ontology to Asthma  
The GCOCD was developed to provide a flexible and reusable framework for modeling chronic diseases, but it lacks specificity when applied to disease-specific contexts such as asthma. The key missing information includes:  
* Asthma-specific symptoms and triggers, such as wheezing, airway inflammation, and pollen exposure, are absent from the general ontology.
* Environmental and temporal factors, including pollution, humidity, and seasonal variations, which influence asthma severity but are not fully captured in GCOCD.
* Granular treatment pathways, as the ontology does not distinguish between general chronic disease management and targeted asthma interventions, such as bronchodilators and corticosteroids.  
To address these limitations, the extension process focuses on context-based adaptation and hierarchical refinement, ensuring that the ontology is compatible with GCOCD while accurately modeling asthma-specific knowledge. The detailed construction process is outlined in Section Ontology Construction from Dataset Schema, where targeted modifications will be introduced to specialize symptoms, treatment pathways, and environmental factors relevant to asthma.  
Ontology Construction from Dataset Schema  
To refine the ontology structure, dataset-driven approaches were used:  
* Feature-Based Extension Using Relevant Parameters  
Key features were prioritized based on their impact on asthma severity and treatment. Feature selection was performed based on AI algorithms to be further discussed in Section 4.4.  
* Context-Specific Data Integration  
Most of the parameters present in the general ontology are reused in the extended asthma ontology: temporal, environmental, medical and organizational.  
* Analyze the dataset to extract key features.
* Demographics: Includes attributes such as Age, Gender, Ethnicity, Education Level, and BMI.
* Symptoms: Specific symptoms such as Chest Tightness, Coughing, and Night-time symptoms.
* Triggers: Identify triggers such as Exercise-Induced and environmental factors (if present in other columns).
* Treatments: Information about diagnosis and treatments.
* Map dataset features to ontology.
* Classes:* [noitemsep,topsep=0 pt]
* Patient: Represents individuals with attributes such as Age, Gender, Ethnicity and BMI.
* Symptoms: Includes asthma-specific subclasses such as Chest Tightness, Coughing and Night-time Symptoms.
* Triggers: Includes subclasses such as Exercise Induced and potentially environmental conditions if described elsewhere.
* Diagnosis: Links patients to identified chronic diseases (e.g., asthma).
* Relationships:
* Patient \(\rightarrow\) has Symptom \(\rightarrow\) Symptom.
* Symptom \(\rightarrow\) isTriggeredBy \(\rightarrow\) Trigger.
* Patient \(\rightarrow\) isDiagnosedWith \(\rightarrow\) Diagnosis.
* Create relationships for specific asthma ontology.
* Example relationships include:
* [noitemsep,topsep=0 pt]
* Patient 5034 hasSymptom Chest Tightness\"
* \"Chest Tightness isTriggeredBy Exercise\".  
To demonstrate the GCOCD, we conducted a preliminary adaptation to the domain of diabetes. The core structure of GCOCD, including the categories of patient data, environmental factors, temporal context, and organizational context, was reused without modification. New disease-specific classes and properties were added to represent glucose level monitoring, insulin treatment, and dietary control. This quick adaptation illustrates the framework's flexibility and its potential to model diverse chronic conditions. A more comprehensive diabetes-specific ontology will be presented in future work, but this exercise confirms GCOCD's reusability across domains.