1 Introduction
2 Literature Review
3 Classification of Ontologies
4 Designing a COVID-19 Ontology
4.1 Determine the Domain and Scope of the Ontology
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What is the domain that the ontology will cover?The COVID-19 is the main domain this ontology covers.
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What is the purpose of this ontology?The purpose of this ontology is to facilitate the gathering and publication of COVID-19–related data as semantic services. Our ontology tracks COVID-19 patient’s medical status and predicts their severity level for a better understanding of the nature of the virus and how patients could be treated. It also tries to reduce the risk of contracting COVID-19 by tracking the essential safety and control measures taken by people.
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Who will use the ontology?This ontology can be used by organizations willing to collect COVID-19–related data to help control this pandemic and know more about this disease, such as hospitals, government agencies, health organizations and researchers.
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What types of questions should the information in the ontology answer?1)What are the most common symptoms of patients with COVID-19?2)How severe is a patient’s case?3)Are the safety measures effective in protecting against contracting COVID-19?4)Is there a relationship between COVID-19 and a certain disease?
4.2 Reuse the Ontology
4.3 Develop a Conceptual Model
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Enumerate key terms in the ontology.The crucial terms that describe a context need to be defined. These terms include nouns that represent a specific concept (e.g., a patient is described by the noun “Patient”), attributes that describe the type and value of what is being modelled (e.g., the value of temperature is a float), verbs that describe the relationships between nouns or between nouns and attributes (e.g., a patient “is a” Person). Since standard terminology shall be used t model medical terms, we used SNOMED CT ontology in building our ontology to model the concepts of drugs and symptoms.
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Define classes and class hierarchy.This step starts by defining the classes used in the ontology, then defining the taxonomy of these classes by matching subclasses to classes.
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Define class properties.The two main class properties are object and datatype properties. These are used to model the relationships among different elements of the ontology, as classes alone do not provide enough information to represent the context behind this ontology. Object properties build relationships between classes by specifying the class domain of the relationship and its class range. The datatype property models the value and type of the concept, such as string, integer, and boolean.
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Define the facet of slots.According to [7], a slot shall be assigned different kinds of facets that frame its value type, allowed values and cardinalities, to be added as required. They are mostly represented as string, integer, and float in our ontology.
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Create instances.Individuals of a certain class are created by choosing a class, then filling the value slots. An example is creating Steve as an instance of Patient.
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Develop our ontology domain.We model our COVID-19 medical ontology with respect to the information and guidelines provided by WHO. It contains information related to a patient with COVID-19, including symptoms and treatment, the patient’s medical history and the safety measures to control this virus by healthy people, patients, or healthcare workers. Our ontology consists of four main sub-ontologies, as presented in Fig. 1.
4.4 Implement the Ontology
4.5 Evaluate the Ontology
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Consistency is achieved when the ontology’s set of definitions and axioms have no contradictions between them, according to [27]. Running Pellet reasoner shows that our ontology is consistent and coherent with no conflicting knowledge.
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Completeness occurs when the represented knowledge by the ontology covers the domain it represents sufficiently [27]. Our ontology is complete in terms of its purposes and constraints. However, COVID-19 is still a new disease that is continuously studied, and not enough confirmed information exists. Hence, from this aspect, our ontology can be considered incomplete.
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Conciseness ensures that the ontology has no redundancy, as stated by [7]. We tried to minimize the number of definitions of our ontology to eliminate redundant ideas while representing the idea fairly. Hence, our ontology is concise.
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Expandability measures whether the ontology can be expanded to describe further knowledge without affecting the current, built ontology. Since much knowledge can still be discovered in COVID-19 disease, we built our ontology such that the core concepts are not altered if new knowledge is added, much as how we added the vaccination part to our initial ontology after vaccination data surfaced. Hence, our ontology is expandable.
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Sensitiveness indicates if any changes could affect the core of the ontology. As mentioned previously, any alterations or addition of new concepts will not affect our representation as in our classes and axioms; hence, our ontology is considered non-sensitive.
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The size of vocabulary (SOV) represents the overall number of classes, properties and individuals in our ontology. In our case, SOV is approximately 300, which is low, thus indicating that our ontology is not significantly large or complex.
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The edge–node ratio (ENR) represents the ratio of the number of edges to the number of nodes. The ENR of our ontology is one, hence indicating that the ontology is simple and straight-forward.
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Tree impurity (TIP) measures the divergence of the ontology inheritance hierarchy. The TIP of our ontology is approximately 0.5, which means that the inheritance hierarchy of our ontology has not deviated significantly from the rooted tree, implying that our ontology is not complex.
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The entropy of ontology graph (EOG) measures the number of structural models. A low EOG denotes more than one structural model, thus a less difficult ontology. Calculated using the formula mentioned in [7], our EOG is almost one, which means that the class structure is fine.
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The number of classes (NOC) Our NOC is 76, which is relatively good.
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The number of properties (NOP) Our NOP is roughly 130, which indicates that the ontology has strong reasoning.
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The number of root classes (NORC) Our NORC is 14, indicating that our ontology is diverse.
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Relationship richness (RR) measures the overall number of relationships divided by the overall sum of numbers of subclasses and relationships. Our RR is approximately 0.5, indicating the richness of our ontology with COVID-19–related content.