Discipline that deals with the processes of storing and transferring information. It attempts to bring together concepts and methods from such varied disciplines as library science, computer science and engineering, linguistics, and psychology to develop techniques and devices to aid in the handling of information. In its early stages in the 1960s, information science was concerned primarily with applying the then-new computer technology to the processing and managing of documents. The applied computer technologies and theoretical studies of information science have since permeated many other disciplines. Computer science and engineering still tend to absorb its theory- and technology-oriented subjects, and management science tends to absorb information-systems subjects.
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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.
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.
Common components of ontologies include:
Ontologies are commonly encoded using ontology languages.
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.
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.
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.
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 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?"
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.
The following are static libraries of human-selected ontologies.
The following are both directories and search engines. They include crawlers searching the Web for well-formed ontologies.