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In "computer science and "information science, an ontology encompasses a "representation, formal naming, and "definition of the "categories, "properties, and "relations of the "concepts, "data, and "entities that substantiate one, many, or all "domains.

Every "field creates ontologies to limit "complexity and organize "information into "data and "knowledge. As new ontologies are made, their use hopefully improves "problem solving within that domain. Translating "research papers within every field is a problem made easier when "experts from different countries maintain a "controlled vocabulary of "jargon between each of their languages.[1]

Since "Google started an initiative called "Knowledge Graph, a substantial amount of research has gone on using the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is often used as synonym for ontology.[2]

Contents

Etymology[edit]

The "compound word ontology combines onto-, from the "Greek ὄν, on ("gen. ὄντος, ontos), i.e. "being; that which is", which is the "present "participle of the "verb εἰμί, eimí, i.e. "to be, I am", and -λογία, -logia, i.e. "logical discourse", see "classical compounds for this type of word formation.[3][4]

While the "etymology is Greek, the oldest extant record of the word itself, the "New Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by "Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by "Rudolf Göckel (Goclenius).

The first occurrence in English of ontology as recorded by the OED (Oxford English Dictionary, online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy by Gideon Harvey.

Overview[edit]

What ontologies in both "information science and "philosophy have in common is the attempt to represent entities, ideas, and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of "ontology engineering (e.g., "Quine and "Kripke in philosophy, "Sowa and "Guarino in computer science),[5] and debates concerning to what extent "normative ontology is possible (e.g., "foundationalism and "coherentism in philosophy, "BFO and "Cyc in artificial intelligence). "Applied ontology is considered a spiritual successor to prior work in philosophy, however many current efforts are more concerned with establishing "controlled vocabularies of narrow domains than "first principles, the existence of "fixed essences, or whether enduring objects (e.g., "perdurantism and "endurantism) may be ontologically more primary than "processes.

Every field uses ontological assumptions to frame explicit theories, research, and applications. For instance, the "definition and ontology of economics is a primacy concern in "Marxist economics[6], but also in other "subfields of economics.[7] An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what "capital assets are at risk and by how much (see "risk management).

"Artificial intelligence has retained the most attention regarding "applied ontology in subfields like "natural language processing within "machine translation and "knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI.[8]

History[edit]

Ontologies arise out of the branch of "philosophy known as "metaphysics, which deals with questions like "what exists?" and "what is the nature of reality?" One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities, and relations such as those between "particulars and "universals, "intrinsic and extrinsic properties, or "essence and "existence. Metaphysics has been an ongoing topic of discussion since recorded history.

Since the mid-1970s, researchers in the field of "artificial intelligence (AI) have recognized that "knowledge engineering is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as "computational models that enable certain kinds of "automated reasoning, which was only "marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of "knowledge-based systems. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy.[9]

In the early 1990s, the widely cited Web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by "Tom Gruber[10] is credited with a deliberate definition of ontology as a technical term in "computer science. Gruber introduced the term as a specification of a conceptualization:

An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy.[11]

Attempting to distance ontologies from taxonomies and similar efforts in "knowledge modeling that rely on "classes and "inheritance, Gruber stated (1993):

Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to "conservative definitions — that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world.[12] To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms.[13]

As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity."[14]

Components[edit]

Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. 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, "classes in programming, "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 those 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.

Types[edit]

Domain ontology[edit]

A domain ontology (or domain-specific ontology) represents concepts which belong to a part of the world, such as biology or politics. Each domain ontology typically models domain specific definitions of terms. 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 ""punched card" and ""video card" meanings.

Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.).

At present, merging ontologies that are not developed from a common "upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effort. There are studies on generalized techniques for merging ontologies,[15] but this area of research is still ongoing, and it's a recent event to see the issue sidestepped by having multiple domain ontologies using the same upper ontology like the OBO foundry.

Upper ontology[edit]

An "upper ontology (or foundation ontology) is a model of the common relations and objects that are generally applicable across a wide range of domain ontologies. It usually employs a "core glossary that contains the terms and associated object descriptions as they are used in various relevant domain ontologies.

Standardized upper ontologies available for use include "BFO, "BORO method, "Dublin Core, "GFO, "OpenCyc/"ResearchCyc, "SUMO, "UMBEL, the Unified Foundational Ontology (UFO),[16] and "DOLCE.[17][18] "WordNet has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontologies.[19]

Hybrid ontology[edit]

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

Visualization[edit]

A survey of ontology visualization techniques is presented by Katifori et al.[20] The most established ontology visualization techniques, namely indented tree and graph are evaluated by Fu et al.[21] A visual language for ontologies represented in "OWL is specified by the Visual Notation for OWL Ontologies (VOWL).[22]

Engineering[edit]

"Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain.[23] It is a subfield of "knowledge engineering that studies the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tools and languages that support them.[24][25]

Ontology engineering aims to make explicit the knowledge contained in software applications, and organizational procedures for a particular domain. Ontology engineering offers a direction for overcoming semantic obstacles, such as those related to the definitions of business terms and software classes. Known challenges with ontology engineering include:

  1. Ensuring the ontology is current with "domain knowledge and term use
  2. Providing sufficient specificity and concept coverage for the domain of interest, thus minimizing the "content completeness problem
  3. Ensuring the ontology can support its use cases

Editors[edit]

Ontology editors are applications designed to assist in the creation or manipulation of ontologies. It's common for ontology editors to use one or more "ontology languages.

Aspects of ontology editors include: the visual navigation possibilities within the "knowledge model, "inference engines and "information extraction, support for modules, import & export foreign "knowledge representation languages for "ontology matching, and the support of meta-ontologies such as "OWL-S, "Dublin Core, etc.[26]

Learning[edit]

Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process. Information extraction and text mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges.[27]

Languages[edit]

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

Published examples[edit]

The W3C "Linking Open Data community project coordinates attempts to converge different ontologies into worldwide "Semantic Web.

Libraries[edit]

The development of ontologies has led to the emergence of services providing lists or directories of ontologies called ontology libraries.

The following are libraries of human-selected ontologies.

The following are both directories and search engines.

Examples of applications[edit]

In general, ontologies can be used beneficially in several fields.

See also[edit]

Related philosophical concepts

References[edit]

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