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Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations.
Common components of ontologies include:
Ontologies are commonly encoded using ontology languages.
Individuals (instances) are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract individuals such as numbers and words (although there are differences of opinion as to whether numbers and words are classes or individuals). Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology.
In formal extensional ontologies, only the utterances of words and numbers are considered individuals – the numbers and names themselves are classes. In a 4D ontology, an individual is identified by its spatio-temporal extent. Examples of formal extensional ontologies are BORO, ISO 15926 and the model in development by the IDEAS Group.
Objects in an ontology can be described by relating them to other things, typically aspects or parts. These related things are often called attributes, although they may be independent things. Each attribute can be a class or an individual. The kind of object and the kind of attribute determine the kind of relation between them. A relation between an object and an attribute express a fact that is specific to the object to which it is related. For example, the Ford Explorer object has attributes such as:
The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing.
Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes). If that is not the case, then you would have either a taxonomy (if hyponym relationships exist between concepts) or a controlled vocabulary. These are useful, but are not considered true ontologies.
Relations (also known as relationships) between objects in an ontology specify how objects are related to other objects. Typically a relation is of a particular type (or class) that specifies in what sense the object is related to the other object in the ontology. For example, in the ontology that contains the concept Ford Explorer and the concept Ford Bronco might be related by a relation of type ⟨is defined as a successor of⟩. The full expression of that fact then becomes:
This tells us that the Explorer is the model that replaced the Bronco. This example also illustrates that the relation has a direction of expression. The inverse expression expresses the same fact, but with a reverse phrase in natural language.
Much of the power of ontologies comes from the ability to describe relations. Together, the set of relations describes the semantics of the domain: that is, its various semantic relations, such as synonymy, hyponymy and hypernymy, coordinate relation, and others. The set of used relation types (classes of relations) and their subsumption hierarchy describe the expression power of the language in which the ontology is expressed.
An important type of relation is the subsumption relation (is-a-superclass-of, the converse of is-a , is-a-subtype-of or is-a-subclass-of). This defines which objects are classified by which class. For example, we have already seen that the class Ford Explorer is-a-subclass-of 4-Wheel Drive Car, which in turn is-a-subclass-of Car.
The addition of the is-a-subclass-of relationships creates a taxonomy; a tree-like structure (or, more generally, a partially ordered set) that clearly depicts how objects relate to one another. In such a structure, each object is the 'child' of a 'parent class' (Some languages restrict the is-a-subclass-of relationship to one parent for all nodes, but many do not).
Another common type of relations is the mereology relation, written as part-of, that represents how objects combine to form composite objects. For example, if we extended our example ontology to include concepts like Steering Wheel, we would say that a "Steering Wheel is-by-definition-a-part-of-a Ford Explorer" since a steering wheel is always one of the components of a Ford Explorer. If we introduce meronymy relationships to our ontology, the hierarchy that emerges is no longer able to be held in a simple tree-like structure since now members can appear under more than one parent or branch. Instead this new structure that emerges is known as a directed acyclic graph.
As well as the standard is-a-subclass-of and is-by-definition-a-part-of-a relations, ontologies often include additional types of relations that further refine the semantics they model. Ontologies might distinguish between different categories of relation types. For example:
Relation types are sometimes domain-specific and are then used to store specific kinds of facts or to answer particular types of questions. If the definitions of the relation types are included in an ontology, then the ontology defines its own ontology definition language. An example of an ontology that defines its own relation types and distinguishes between various categories of relation types is the Gellish ontology.
For example, in the domain of automobiles, we might need a made-in type relationship which tells us where each car is built. So the Ford Explorer is made-in Louisville. The ontology may also know that Louisville is-located-in Kentucky and Kentucky is-classified-as-a state and is-a-part-of the U.S. Software using this ontology could now answer a question like "which cars are made in the U.S.?"
In object-oriented programming, a class defines the shared aspects of objects created from the class. The capabilities of a class differ between programming languages, but generally the shared aspects consist of state (variables) and behavior (methods) that are each either associated with an particular object or with all objects of that class.
Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.
In any of several fields of study that treat the use of signs — for example, in linguistics, logic, mathematics, semantics, semiotics, and philosophy of language — the extension of a concept, idea, or sign consists of the things to which it applies, in contrast with its comprehension or intension, which consists very roughly of the ideas, properties, or corresponding signs that are implied or suggested by the concept in question.
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.
In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation.
The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.
In knowledge representation and ontology components, including for object-oriented programming and design, is-a is a subsumptive relationship between abstractions, wherein one class A is a subclass of another class B . In other words, type A is a subtype of type B when A's specification implies B's specification. That is, any object that satisfies A's specification also satisfies B's specification, because B's specification is weaker.
A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure of a programming language.
In programming language theory, semantics is the rigorous mathematical study of the meaning of programming languages. Semantics assigns computational meaning to valid strings in a programming language syntax. It is closely related to, and often crosses over with, the semantics of mathematical proofs.
Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. It may be applied as part of broader Model-driven engineering (MDE) concept.
An information model in software engineering is a representation of concepts and the relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse. Typically it specifies relations between kinds of things, but may also include relations with individual things. It can provide sharable, stable, and organized structure of information requirements or knowledge for the domain context.
RDF Schema (Resource Description Framework Schema, variously abbreviated as RDFS, RDF(S), RDF-S, or RDF/S) is a set of classes with certain properties using the RDF extensible knowledge representation data model, providing basic elements for the description of ontologies. It uses various forms of RDF vocabularies, intended to structure RDF resources. RDF and RDFS can be saved in a triplestore, then one can extract some knowledge from them using a query language, like SPARQL.
SNOMED CT or SNOMED Clinical Terms is a systematically organized computer-processable collection of medical terms providing codes, terms, synonyms and definitions used in clinical documentation and reporting. SNOMED CT is considered to be the most comprehensive, multilingual clinical healthcare terminology in the world. The primary purpose of SNOMED CT is to encode the meanings that are used in health information and to support the effective clinical recording of data with the aim of improving patient care. SNOMED CT provides the core general terminology for electronic health records. SNOMED CT comprehensive coverage includes: clinical findings, symptoms, diagnoses, procedures, body structures, organisms and other etiologies, substances, pharmaceuticals, devices and specimens.
In information science and ontology, a classification scheme is an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity to taxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a wide semantic spectrum while taxonomies tend to be devoted to a single topic.
In philosophy, the term formal ontology is used to refer to an ontology defined by axioms in a formal language with the goal to provide an unbiased view on reality, which can help the modeler of domain- or application-specific ontologies to avoid possibly erroneous ontological assumptions encountered in modeling large-scale ontologies.
Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.
The Gellish English Dictionary-Taxonomy is an example of an open-source “smart” electronic dictionary, in which concepts are arranged in a subtype-supertype hierarchy, thus forming a taxonomy. The dictionary-taxonomy is machine readable. It is compliant with the guidelines of ISO 16354. Apart from the fact that it is an English (business-technical) dictionary, it also defines the semantics of Gellish English, which is a computer-interpretable structured subset of the natural English language for data storage and data exchange. The dictionary-taxonomy differs from conventional dictionaries because of several additional capabilities. Therefore it is called "smart." This means that it satisfies the following criteria:
Generic data models are generalizations of conventional data models. They define standardised general relation types, together with the kinds of things that may be related by such a relation type.
In knowledge representation, a class is a collection of individuals or individuals objects. A class can be defined either by extension, or by intension, using what is called in some ontology languages like OWL. According to the type–token distinction, the ontology is divided into individuals, who are real worlds objects, or events, and types, or classes, who are sets of real world objects. Class expressions or definitions gives the properties that the individuals must fulfill to be members of the class. Individuals that fulfill the property are called Instances.
Arabic Ontology is a linguistic ontology for the Arabic language, which can be used as an Arabic WordNet with ontologically clean content. People use it also as a tree of the concepts/meanings of the Arabic terms. It is a formal representation of the concepts that the Arabic terms convey, and its content is ontologically well-founded, and benchmarked to scientific advances and rigorous knowledge sources rather than to speakers' naïve beliefs as wordnets typically do . The Ontology tree can be explored online.