The definition OWL description logic Google AI Overview. Accessed 27 Jul 2026. OWL Description Logic (DL) is a formal knowledge representation language that underpins the Web Ontology Language (OWL), ensuring logical decidability and computational completeness. It uses concepts (classes), roles (properties), and individuals to structure domain knowledge.( Springer Nature Link +3) Core Components • Concepts (Classes): Sets of objects or individuals sharing common properties (e.g., Person). • Roles (Properties): Binary relations linking pairs of individuals (e.g., hasChild). • Individuals: Specific instances or objects belonging to concepts (e.g., John). • Axioms: Statements asserting facts or relationships, such as subclass inclusions (Cat ⊑ Animal). ontology-explained.com +1 Key Features • Decidability: Guarantees that automated reasoning algorithms will always finish running and yield a correct answer. • Constructors: Allow complex concepts to be built using logical operators like intersection, union, and complement (AND, OR, NOT). • Restrictions: Place limits on property values and cardinalities to precisely define class memberships. For critique in relation to NeuroNames scientific ontology, see Discussion in Bowden-2018: "A decade ago, informaticists of the Neuroscience Information Framework (NIF), an organization supported by the Blueprint Consortium of NIH institutes and agencies that support neuroscientific research, set out to create a “web-based structure for identifying, locating, relating, accessing, integrating, and analyzing information from the neuroscience research enterprise.”21 Essential to that effort was a vocabulary for describing resources and their content, and a concept-based query system that could be used to search for neuroscience information across diverse resources. For that purpose they used widely accepted tools and best practices of informatics, which were under development for the Semantic Web.22 That approach uses tools of descriptive logic to define a domain of knowledge by creating an ontology of concepts and compiling a nomenclature of standard names contributed by domain experts. After several years of dedicated attempts, the NIF team concluded that the descriptive logic approach was less than adequate for their needs. 4 “Early in our efforts to build ontologies, we tied ourselves in knots trying to capture all of the class-level rules that define something like [for example] a Purkinje neuron.” They identified two sources of difficulty. First, “…ontology languages such as OWL are not particularly good at dealing with numerical values, such as ranges or probabilities.” An ontology of neuroanatomical structure must accommodate mathematically defined geometric patterns of such attributes as gene expression, neurotransmitter concentration, and fMRI activation, for example. Such patterns are defined digitally by arrays of x,y,z coordinates of voxels or pixels, each of which is tagged for the expression of various attributes. The second source of difficulty they identified was that, “Much of what we observe is colored by the experimental procedures used to prepare and image biological specimens.” Our interpretation of that issue is that an ontology useful for classifying data neuroanatomically must be tied to operational definitions, that is, the definitions of structures as the distribution patterns of attributes, such as gene expression, etc., must include the basic experimental procedures used to detect them. Furthermore, to be useful for automatic interoperability and machine processing, a digital ontology must codify not only the relations of structures to names in the standard nomenclature of the Mediator but also must codify the relations between all synonyms used in different databases and names in the standard nomenclature. Since changes in terminology can occur over time, the ontology must accommodate evolution of preferred names of structural concepts. The NeuroNames ontology was designed to address each of those issues: quantification, operational definitions, and terminological evolution. "