Upper-level Ontology

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An Upper-level Ontology is an ontology that covers high-level concepts (and their interrelationships).



  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Upper_ontology Retrieved:2015-2-8.
    • In information science, an upper ontology (also known as a top-level ontology or foundation ontology) is an ontology (in the sense used in information science) which describes very general concepts that are the same across all knowledge domains. An important function of an upper ontology is to support very broad semantic interoperability between a large number of ontologies which are accessible ranking "under" this upper ontology. As the rank metaphor suggests, it is usually a hierarchy of entities and associated rules (both theorems and regulations) that attempts to describe those general entities that do not belong to a specific problem domain.

      The seemingly conflicting use of metaphors implying a solid rigorous bottom-up "foundation" or a top-down imposition of somewhat arbitrary, and possibly political, decisions is no accident – the field is characterized by the usual mix of controversy, politics, competing approaches and academic rivalry. Some upper ontologies have led to commercial products, causing a financial incentive to promote one ontology over the competing systems.

      Debates notwithstanding, it can be said that a very important part of each upper ontology can be considered as the computational implementation of natural philosophy, which itself is a more empirical method for investigating the topics within the philosophical discipline of physical ontology.

      Library classification systems predate these upper ontology systems. Though library classifications organize and categorize knowledge using general concepts that are the same across all knowledge domains, neither system is a replacement for the other.




Depending on the scope of the ontology, ontology may be classified as follows (see also figure above):
  • upper, generic, top-level ontology - describing general knowledge, such as what is time and what is space
  • domain ontology - describing a domain, such as medical domain or electrical engineering domain, or narrower domains, such as personal computers domain
  • task - ontology suitable for a specific task, such as assembling parts together
  • application - ontology developed for a specific application, such as assembling personal computers



  • http://www-sop.inria.fr/acacia/personnel/phmartin/RDF/phOntology.html
    • Metadata retrieval and reuse is enhanced when metadata providers follow common or interconnected ontologies. Below, we propose a top-level ontology to ease and guide metadata representation and organization. It reuses the classes and properties declared in [RDFMS] and [RDFSchema] and adds about 80 new classes and 120 new relations (properties). Some classes come from the Frame Ontology of Ontolingua [FrameOntol] (not all the classes and relations of this ontology have been reused since many are not relevant to RDF, e.g. n-ary relations with n > 2). Most classes and properties were selected and adapted from works of John Sowa [Sowa84] and completed with classes and relations from various other top-level ontologies (e.g. to a small extent, the CYC top-level ontology and the Generalized Upper Model). Whereas the set of proposed relations can be seen as relatively complete for the representation of most natural language sentences, we have mainly limited the introduction of classes to those required for the signatures of the relations (i.e. to constrain their ranges and domains). The whole file of this top-level ontology is accessible in RDF format and in a more readable format (that is also parsable)

      To complete that work on classes, we have also worked on the WordNet lexical database [WN]. First, we have inserted the WordNet top-level classes into our top-level ontology (cf. section 1.2). Second, we have translated this database (plus the top-level ontology) into a 24Mb RDF file (click here for a 4.2Mb gzipped version). A version of this file in also given in the more readable format (click here for a 3.7Mb gzipped version). Third, to search this ontology of 84,000 categories and navigate along the various kinds of relations between them, we have implemented a CGI script and an HTML+Javascript interface to use it. The results are given in RDF or simpler formats.

      Following our conventions, we have only used singular nouns for class names and have not introduced inverse relations (e.g. subClass and agentOf have not be introduced since subClassOf and agent are have been declared).