information science

An ontology in both computer science and information science is a formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain.

Ontologies are used in artificial intelligence, the Semantic Web, software engineering, biomedical informatics, library science, and information architecture as a form of knowledge representation about the world or some part of it.


The term ontology has its origin in philosophy, where it is the name of one fundamental branch of metaphysics, concerned with analyzing various types or modes of existence, often with special attention to the relations between particulars and universals, between intrinsic and extrinsic properties, and between essence and existence. According to Tom Gruber at Stanford University, the meaning of ontology in the context of computer science is “a description of the concepts and relationships that can exist for an agent or a community of agents.” He goes on to specify that an ontology is generally written, “as a set of definitions of formal vocabulary.”

What ontology has in common in both computer science and philosophy is the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories. In both fields, one finds considerable work on problems of ontological relativity (e.g. Quine and Kripke in philosophy, Sowa and Guarino in computer science and debates concerning whether a normative ontology is viable (e.g. debates over foundationalism in philosophy, debates over the Cyc project in AI).

Differences between the two are largely matters of focus. Philosophers are less concerned with establishing fixed, controlled vocabularies than are researchers in computer science, while computer scientists are less involved in discussions of first principles (such as debating whether there are such things as fixed essences, or whether entities must be ontologically more primary than processes). During the second half of the 20th century, philosophers extensively debated the possible methods or approaches to building ontologies, without actually building any very elaborate ontologies themselves. By contrast, computer scientists were building some large and robust ontologies (such as WordNet and Cyc) with comparatively little debate over how they were built.

In the early years of the 21st century, the interdisciplinary project of cognitive science has been bringing the two circles of scholars closer together. For example, there is talk of a "computational turn in philosophy" which includes philosophers analyzing the formal ontologies of computer science (sometimes even working directly with the software), while researchers in computer science have been making more references to those philosophers who work on ontology (sometimes with direct consequences for their methods). Still, many scholars in both fields are uninvolved in this trend of cognitive science, and continue to work independently of one another, pursuing separately their different concerns.

Ontology building blocks

Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. As mentioned above, most ontologies describe individuals (instances), classes (concepts), attributes, and relations. In this section each of these components is discussed in turn.

Common components of ontologies include:

  • Individuals: instances or objects (the basic or "ground level" objects)
  • Classes: sets, collections, concepts, types of objects, or kinds of things.
  • Attributes: aspects, properties, features, characteristics, or parameters that objects (and classes) can have
  • Relations: ways in which classes and individuals can be related to one another
  • Function terms: complex structures formed from certain relations that can be used in place of an individual term in a statement
  • Restrictions: formally stated descriptions of what must be true in order for some assertion to be accepted as input
  • Rules: statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form
  • Axioms: assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements.
  • Events: the changing of attributes or relations

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 ISO 15926 and the model in development by the IDEAS Group.


Classes – concepts that are also called type, sort, category, and kind – can be defined as an extension or an intension. According to an extensional definition, they are abstract groups, sets, or collections of objects. According to an intensional definition, they are abstract objects that are defined by values of aspects that are constraints for being member of the class. The first definition of class results in ontologies in which a class is a subclass of collection. The second definition of class results in ontologies in which collections and classes are more fundamentally different. Classes may classify individuals, other classes, or a combination of both. Some examples of classes:

  • Person, the class of all people, or the abstract object that can be described by the criteria for being a person.
  • Vehicle, the class of all vehicles, or the abstract object that can be described by the criteria for being a vehicle.
  • Car, the class of all cars, or the abstract object that can be described by the criteria for being a car.
  • Class, representing the class of all classes, or the abstract object that can be described by the criteria for being a class.
  • Thing, representing the class of all things, or the abstract object that can be described by the criteria for being a thing (and not nothing).

Ontologies vary on whether classes can contain other classes, whether a class can belong to itself, whether there is a universal class (that is, a class containing everything), etc. Sometimes restrictions along these lines are made in order to avoid certain well-known paradoxes.

The classes of an ontology may be extensional or intensional in nature. A class is extensional if and only if it is characterized solely by its membership. More precisely, a class C is extensional if and only if for any class C', if C' has exactly the same members as C, then C and C' are identical. If a class does not satisfy this condition, then it is intensional. While extensional classes are more well-behaved and well-understood mathematically, as well as less problematic philosophically, they do not permit the fine grained distinctions that ontologies often need to make. For example, an ontology may want to distinguish between the class of all creatures with a kidney and the class of all creatures with a heart, even if these classes happen to have exactly the same members. In most upper ontologies, the classes are defined intensionally. Intensionally defined classes usually have necessary conditions associated with membership in each class. Some classes may also have sufficient conditions, and in those cases the combination of necessary and sufficient conditions make that class a fully defined class.

Importantly, a class can subsume or be subsumed by other classes; a class subsumed by another is called a subclass (or subtype) of the subsuming class (or supertype). For example, Vehicle subsumes Car, since (necessarily) anything that is a member of the latter class is a member of the former. The subsumption relation is used to create a hierarchy of classes, typically with a maximally general class like Anything at the top, and very specific classes like 2002 Ford Explorer at the bottom. The critically important consequence of the subsumption relation is the inheritance of properties from the parent (subsuming) class to the child (subsumed) class. Thus, anything that is necessarily true of a parent class is also necessarily true of all of its subsumed child classes. In some ontologies, a class is only allowed to have one parent (single inheritance), but in most ontologies, classes are allowed to have any number of parents (multiple inheritance), and in the latter case all necessary properties of each parent are inherited by the subsumed child class. Thus a particular class of animal (HouseCat) may be a child of the class Cat and also a child of the class Pet.

A partition is a set of related classes and associated rules that allow objects to be classified by the appropriate subclass. The rules correspond with the aspect values that distinguish the subclasses from the superclasses. For example, to the right is the partial diagram of an ontology that has a partition of the Car class into the classes 2-Wheel Drive Car and 4-Wheel Drive Car. The partition rule (or subsumption rule) determines if a particular car is classified by the 2-Wheel Drive Car or the 4-Wheel Drive Car class.

If the partition rule(s) guarantee that a single Car cannot be in both classes, then the partition is called a disjoint partition. If the partition rules ensure that every concrete object in the super-class is an instance of at least one of the partition classes, then the partition is called an exhaustive partition.


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:

  • Ford Explorer
  • door (with as minimum and maximum cardinality: 4)
  • {4.0L engine, 4.6L engine}
  • 6-speed transmission

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.


Relationships (also known as relations) 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 . The full expression of that fact then becomes:

  • Ford Explorer is defined as a successor of : Ford Bronco

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. 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.

The most 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 hierarchical 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 meronymy relation, written as part-of, that represents how objects combine together 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, we find that this simple and elegant tree structure quickly becomes complex and significantly more difficult to interpret manually. It is not difficult to understand why; a class of which is described that there is always a member that is a part of a member of another class might also have a member that is part of a member of a third class. Consequently, classes may be part of more than one whole class. The structure that emerges is known as a directed acyclic graph (DAG).

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 for relations between classes
  • relation types for relations between individuals
  • relation types for relations between an individual and a class
  • relation types for relations between a single object and a collection
  • relation types for relations between collections

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 USA. Software using this ontology could now answer a question like "which cars are made in the USA?"

Domain ontologies and upper ontologies

A domain ontology (or domain-specific ontology) models a specific domain, or part of the world. It represents the particular meanings of terms as they apply to that domain. For example the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punch card" and "video card" meanings.

An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It contains a core glossary in whose terms objects in a set of domains can be described. There are several standardized upper ontologies available for use, including Dublin Core, GFO, OpenCyc/ResearchCyc, SUMO, and DOLCEl. WordNet, while considered an upper ontology by some, is not an ontology: it is a unique combination of a taxonomy and a controlled vocabulary (see above, under Attributes).

The Gellish ontology is an example of a combination of an upper and a domain ontology.

Since domain ontologies represent concepts in very specific and often eclectic ways, they are often incompatible. As systems that rely on domain ontologies expand, they often need to merge domain ontologies into a more general representation. This presents a challenge to the ontology designer. Different ontologies in the same domain can also arise due to different perceptions of the domain based on cultural background, education, ideology, or because a different representation language was chosen.

At present, merging ontologies is a largely manual process and therefore time-consuming and expensive. Using a foundation ontology to provide a common definition of core terms can make this process manageable. There are studies on generalized techniques for merging ontologies, but this area of research is still largely theoretical.

Ontology languages

An ontology language is a formal language used to encode the ontology. There are a number of such languages for ontologies, both proprietary and standards-based:

Examples of published ontologies

  • Basic Formal Ontology (BFO , a formal upper ontology designed to support scientific research
  • CCO The Cell-Cycle Ontology is an application ontology that represents the cell cycle
  • CIDOC Conceptual Reference Model (CIDOC CRM), an ontology for cultural heritage
  • COPSMO, an OWL ontology that is a merger of the basic elements of the OpenCyc and SUMO ontologies, with additional elements.
  • Cyc for formal representation of the universe of discourse.
  • Dublin Core, a simple ontology for documents and publishing.
  • Foundational, Core and Linguistic Ontologies
  • Foundational Model of Anatomy[for human anatomy
  • Gene Ontology for genomics
  • Generalized Upper Model, a linguistically-motivated ontology for mediating between clients systems and natural language technology
  • Gellish English dictionary, an ontology that includes a dictionary and taxonomy that includes an upper ontology and a lower ontology that focusses on industrial and business applications in engineering, technology and procurement. See also Gellish as Open Source project on SourceForge.
  • GOLD General Ontology for Linguistic Description
  • IDEAS Group A formal ontology for enterprise architecture being developed by the Australian, Canadian, UK and U.S. Defence Depts.
  • Linkbase A formal representation of the biomedical domain, founded upon Basic Formal Ontology (BFO)
  • LPL Lawson Pattern Language
  • OBO Foundry: a suite of interoperable reference ontologies in biomedicine.
  • Ontology for Biomedical Investigations is an open access, integrated ontology for the description of biological and clinical investigations.
  • Plant Ontology for plant structures and growth/development stages, etc.
  • POPE Purdue Ontology for Pharmaceutical Engineering
  • PRO, the Protein Ontology of the Protein Information Resource, Georgetown University.
  • Program abstraction taxonomy program abstraction taxonomy
  • Protein Ontology for proteomics
  • SBO, the Systems Biology Ontology, for computational models in biology
  • Suggested Upper Merged Ontology, which is a formal upper ontology
  • SWEET Semantic Web for Earth and Environmental Terminology
  • ThoughtTreasure ontology
  • TIME-ITEM Topics for Indexing Medical Education
  • WordNet Lexical reference system

Ontology libraries

The development of ontologies for the Web has led to the apparition of services providing lists or directories of ontologies with search facility. Such directories have been called ontology libraries.

The following are static libraries of human-selected ontologies.

  • DAML Ontology Library maintains a legacy of ontologies in DAML.
  • Protege Ontology Library contains a set of owl, Frame-based and other format ontologies.
  • SchemaWeb is a directory of RDF schemata expressed in RDFS, OWL and DAML+OIL.

The following are both directories and search engines. They include crawlers searching the Web for well-formed ontologies.

  • OBO Foundry / Bioportal is a suite of interoperable reference ontologies in biology and biomedicine.
  • OntoSelect Ontology Library offers similar services for RDF/S, DAML and OWL ontologies.
  • Ontaria is a "searchable and browsable directory of semantic web data", with a focus on RDF vocabularies with OWL ontologies.
  • Swoogle is a directory and search engine for all RDF resources available on the Web, including ontologies.

See also


Further Reading

External links

General links

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