SNOMED CT is built on the design requirements for reference clinical terminology outlined in 1998 and expanded in 2001 in a landmark article—“Desiderata for controlled medical vocabularies in the twenty-first century” by James J. Cimino [6] . These requirements can be summarized as follows: 1) content importance, 2) concept consideration, 3) concept permanence, 4) non-significant concept identifier, 5) poly-hierarchy, 6) formal definitions, 7) rejection of the “not classified elsewhere” notion, 8) multiple granularity, 9) multiple and coherent viewpoints, 10) context representation, 11) capability to evolve, and 12) consideration of redundancy.

In these Desideratas, James J. Cimino talks about the completeness of vocabulary, that is, domain coverage. In this respect, SNOMED CT can boast a broad coverage of the medical and health field by having about 350,000 concepts that cover anatomy, pathologies, observable variables, organisms, pharmaceutical and biological products, interventions, etc. (Desiderata Cimino 1).

According to SNOMED, a concept is defined as “a unit of knowledge created by a unique combination of characters”. It has a preferred name and several synonyms in every language available in SNOMED CT, including French (Desiderata Cimino 2 and 3).

Each concept is associated with a non-significant identifier of a positive 64-bit integer type, having a minimum and maximum authorized length of 6 and 18 digits respectively (Desiderata Cimino 4) [9] .

The primary hierarchical relation of SNOMED CT is the “is a” relation, forming the base of SNOMED CT hierarchies with a root concept that has no concept above it. A concept with a “is a” relation to a parent concept (more general concept) is called a descendant concept. SNOMED CT allows a concept to have more than one “is a” relation with other concepts, indicating that SNOMED CT has a poly-hierarchical structure (Desiderata Cimino 5).

Unlike aggregations or classifications where all considered domain objects must be classified in a single exclusive and exhaustive place, SNOMED CT is not bound by these requirements. Domain objects are added as needed and from different viewpoints (Desiderata Cimino 9 and 11). There are no concepts like “others, unspecified or not classified elsewhere” (Desiderata Cimino 7).

SNOMED CT has evolved by expanding its coverage of the medical and health field at the contextual level. Initially, SNOMED CT representations used only three hierarchies, but now it has 19, allowing the collection of information not only on specific clinical situations but also on the context (Desiderata Cimino 10) [10] .

We often encounter similar terms or concepts at different levels of SNOMED CT, known as redundancy. This redundancy allows more choices when using SNOMED CT (Desiderata Cimino 12). Various mechanisms within SNOMED CT account for this redundancy, including synonymy, concept hierarchy, and concept equivalence.

Lastly, for some hierarchies, SNOMED CT provides a formal ontological representation, which we will address in the following chapter (Desiderata Cimino 6).

Ontology is a branch of philosophy that can be defined as the study of what “IS” in the physical world [7] . The application of this approach in digital information sciences, especially in health, began in the 1990s as a representation of information entities used in a knowledge base. The most widely cited definition of formal digital ontology is that of Gruber [11] : “An ontology is an explicit specification of a conceptualization.” By conceptualization, Gruber means an abstract and simplified view of the world’s representation, and by explicit specification, he means that this conceptualization is made unambiguously in a concrete language. Gruber defined five types of components for describing a domain’s knowledge: 1) concepts, 2) relations, 3) functions, 4) axioms, and 5) instances. Later, Gruber’s definition was supplemented by Studer and his colleagues [12] , stating that a formal ontology is a formal and explicit specification of a shared conceptualization. The term “formal” implies that the applied ontology should be machine-readable, meaning machines should be able to interpret the semantics or the meaning of the provided information.

Formal digital ontology has found its place in the biomedical field, confronted with an explosion of knowledge contained in heterogeneous terminological systems [13] [14] . In this regard, SNOMED CT, for certain hierarchies, provides a formal ontological representation that expresses the formal logic of a “Concept Model” following the rules of “compositional grammar” [3] in a subset or profile of OWL 2 named EL for Expression Language, offering application possibilities [15] [16] [17] [18] . For example, the disease “acute intestinal infection due to Escherichia coli”, represented by the concept 111839008 |Intestinal infection caused by Escherichia coli (disorder)|, is equivalent to the logical expression using the following OWL functional syntax, see Figure 1.

However, the OWL2 logical profile of SNOMED CT does not cover all the expressive possibilities of EL OWL2 [15] for several reasons, among which complexity issues are found.

We provide a comparative table of the constructors and entities of the EL logical profile of SNOMED CT and the OWL2 specification (see Table 1).

Table 2 below illustrates the proportion of usage of the constructors of the EL OWL2 logical profile.