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Added by Jose REMY, last edited by Jose REMY on Feb 18, 2008

( DESC 'ontologies' )

openOSI ontologies name space - OID for ontologies

Dublin Core elements and element refinements
This object identifier (OID) describes ontologies.

ASN1 notation: {iso(1) identified-organization(3) dod(6) internet(1) private(4) enterprise(1) openosi(27630) ontologies(3)}
URN notation: urn:oid:
IETF DOT notation:
BNF notation (RFC822 Backus-Naur form): ( DESC 'ontologies' )
Description:  ontologies description



  • Concept : abstract mental representation of an idea or un object
  • Metadata : Data about data
  • Semantic : Meaning of data
  • Name space Defined space with a label (a name), where Semantic is univocal and not ambiguous
  • Relationship link between concepts of a hierarchical or subsumption nature
  • Subsumption When C subsume C' whe have C' "included or equal in" C or(and) C = C' "union" C'', that is C contains C'

Inspired from Ontology-en and Ontologie-fr

In this name space, ontologies are semantic Metadata about concepts with relationships mostly hierarchical, both represented by formal description of described data (XML schema and OWL description). Metadata are data about data. An item of metadata may describe an individual datum, or content item, or a collection of data including multiple content items. Ontologies Metadata focus on semantic attached to a given structured named space. When structured into a hierarchical arrangement with rules and properties, metadata are more properly called ontologies. This term describes "what exists" for some purpose or to enable some action in the community accepting the related named space. Plural form "ontologies" related to objects is preferred to 'Ontology", clarifying distinction with philosophical term "Ontology" which define a Philosophical category.

In artificial sciences (engineering), an "ontologies set" is a data model that represents a set of concepts and relationships within a domain identified by a named space.

Common components of ontologies include:

  • Individuals: the basic or "ground level" objects
  • Classes: sets, collections, or types of objects
  • Attributes: properties, features, characteristics, or parameters that objects can have and share
  • Relations: ways that objects can be related to one another
  • Events: the changing of attributes or relations


  • 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
  • Axiom: assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes, for its domain of application

These refinements distinguish Ontologies from Taxonomy (hierarchical structures).

Related Metadata: Dublin Core definition 

The 15 standardized Metadata elements approved as ISO 15836:2003 [2].

  1. Title
  2. Creator
  3. Subject
  4. Description
  5. Publisher
  6. Contributor
  7. Date
  8. Type
  9. Format
  10. Identifier
  11. Source
  12. Language
  13. Relation
  14. Coverage
  15. Rights

Additional 7 elements for Qualified Dublin core

  1. Audience
  2. Provenance
  3. RightsHolder
  4. InstructionalMethod GEM controlled vocabulary
  5. AccrualMethod
  6. AccrualPeriodicity
  7. AccrualPolicy


Inspired from Metadata-en, Matadonnees-fr

Ontologies are described using Metadata and conceptual models.

Metadata are used to facilitate the understanding, use and management of data (identification and classification). The metadata required for effective data management varies with the type of data and context of use. Ontologies are dedicated to computerized knowledge management systems. Automatic systems handling cognition processes have efficiency based on knowledge representation. Therefore Ontologies support efficiency of cognition processes as software services. In system of systems architecture, ontologies allow for knowledge sharing between different systems. In human-machine hybrid systems, ontologies also allow for knowledge sharing.

Conceptual models are used to provide a formal definition of ontologies, that is classification or categorizing the set of concepts (domain), building the overall concept to describe, with the description of relations and attributes. Conceptual models are sometime called "weak ontologies" when they don't embed semantic logic, that is when they only use object modeling language like UML (Unified Modeling Language). Opposed "strong ontologies" embed semantic logic with node and link structured languages (Frame based), or axiom and rule structured languages (Axiomatic); these strong ontologies are sometime called "Logical theories". Ontologies are commonly encoded using XML ontologies languages such as RDF (Resource Description Framework) and OWL (Web Ontology Language).

In a library, where the data is the content of the titles stocked, metadata about a title would typically include a description of the content, the author, the publication date and the physical location. In the context of a camera, where the data is the photographic image, metadata would typically include the date the photograph was taken and details of the camera settings. On a portable music player such as an Apple iPod, the album names, song titles and album art embedded in the music files are used to generate the artist and song listings, and are metadata. In the context of an information system, where the data is the content of the computer files, metadata about an individual data item would typically include the name of the field and its length. Metadata about a collection of data items, a computer file, might typically include the name of the file, the type of file and the name of the data administrator. When Metadata contain semantic, as with ontologies, they are used to build models and for reasoning. In the context of political sciences, where the data are collections of facts, metadata about facts would typically include date, meaning, observer position, language, related conceptual model (epistemology).

Metadata are frequently stored in a central location and used to help organizations and open systems standardize their data. This information is typically stored in a Metadata registry as this one. Metadata 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. They are used to reason about the objects within that domain.

For instance, the arrangement using ontologies of subject headings in a library catalog serves not only as a guide to finding books on a particular subject in the stacks, but also as a guide to what subjects "exist" in the library's own ontology and how more specialized topics are related to or derived from the more general subject headings. From automatic data processing point of view, ontologies will allow query system to make a distinction between multiple possible usages of a single word (Author last name, Title content, text content, Editor name, Library name, ....). This disambiguation capability is mainly useful when queriyng data stores with different format, for instance over Internet.



XML format

	<asn1-notation>{iso(1) identified-organization(3) dod(6) internet(1) private(4) enterprise(1) openosi(27630) ontologies(3)}</asn1-notation>
	<description> openOSI ontologies name space - ontologies description</description>
	<information>More <i>information</i> can be found in<a href="http://openosi.org/osi/display/oid/">openOSI ontologies name space - Ontologies</a> </information>

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