## 3 Methodology for Designing General Contextual Chronic Disease Ontology
### Designing General Contextual Ontology: Contextual Insights from Dataset Analysis  
Define the scope domain of ontology: The ontology focuses on chronic diseases and aims to facilitate data gathering and representation.  
The questions guiding the ontology's domain include:  
* What is the domain the ontology will cover?  
Chronic diseases are the focus, addressing conditions such as asthma, diabetes and hypertension.
* What is the purpose of this ontology?  
To aid in collecting and representing chronic disease data for predictive diagnosis and healthcare research.
* Who will use the ontology?  
Hospitals, healthcare professionals, researchers and organizations conducting disease-related studies.  
* What types of questions should the information in the ontology answer? Is this patient diagnosed with a chronic disease? What are the symptoms of this disease?
* The domain scope integrates temporal, environmental, organizational and medical contexts to comprehensively model chronic diseases.  
Developing the conceptual model following [PERSON]'s methodology, the ontology is structured using context categorization. Identify key terms in the ontology:  
* Nouns (Concepts): Patient, Physician, Symptom, Treatment, Diagnosis, Allergy.
* Attributes (Attributes of Concepts): Age, Gender, Diagnosis Date, Environmental Factors (e.g., pollution levels).
* Verbs (Relationships): \"hasSymptom\" (Patient \(\rightarrow\) Symptom), \"isTreatedBy\" (Disease \(\rightarrow\) Treatment), \"isTriggeredBy\" (Symptom \(\rightarrow\) Environmental Factor).
* Standardization: SNOMED CT is used for consistent representation of symptoms and medical terms.
* Classes and their hierarchy: Top-Level Classes: Temporal Context, Environmental Context, Medical Data, Organizational Context, Events.
* Subclasses: Under medical data: Symptoms, Treatments, Diagnoses, Allergies. Under environmental Context: Pollution, Temperature, Humidity.
* Define class properties:
* Object Properties: hasSymptom: Domain = Patient, Range = Symptom. isTreatedBy: Domain = Symptom, Range = Treatment.
* Datatype Properties: age: Integer, diagnosisDate: Date.
* Define Slot Facets: Assign value types, permitted values properties:
* Age: Integer (1 to 100)
* PollutionLevel: Float (0.0 to 500.0)
* Create Instances: Populate slots with real-world values from datasets: A patient named \"[PERSON]\" with: Age = 45, Gender = Male, Symptom = \"Chest Tightness\".  
[PERSON]'s Methodology with a Comparative Perspective  
Although several ontology development methodologies exist, such as METHONTOL-OGY, Ontolingua, and the 101 Method, many fall short in addressing long-term reusability and modular extension. [PERSON]'s methodology was selected for its ability to integrate structured development steps with adaptability across medical domains [53; 58]. [PERSON]'s method combines conceptual modeling, class-property definition, and real-world dataset mapping, making it a strong fit for developing a general ontology that can later be extended to specific diseases like asthma. Moreover, [PERSON]'s method supports ontology lifecycle planning and enables context categorization, which is essential for chronic diseases influenced by environmental, organizational, and temporal dimensions.